This study used 15 Coupled Model Intercomparison Project Phase 6 (CMIP6) models to analyze the mean and extreme projection precipitation during the Indian summer monsoon (ISM) season, i.e., June to September (JJAS). A continuous rise in precipitation and its associated unpredictability were predicted by global climate models using the CMIP6. The extreme values are estimated on the basis of generalized extreme value (GEV) distribution. Four socioeconomic paths are used as SSP126, SSP245, SSP370 and SSP585 to understand the impact of low-emission to high-emission scenarios. In all the scenarios, it is seen that all the models predict a significant rise in JJAS mean and extreme precipitation. Moreover, a strong correlation is obtained between the historical (1995–2014) and near-emission future simulation (2021–2040), mid-emission future simulation (2041–2060), high-emission future simulations (2081–2100) of air temperature and precipitation. In extreme emission scenarios, ensemble mean of CMIP6 models shows an increasing amplitude in surface air temperature and precipitation over India in the near future (2021–2040), mid future (2041–2060) and the end of the century (2081–2100). The CMIP6 simulations mainly support the results of all models, but they also demonstrate improved robustness across models.

  • Projection of mean and extreme precipitation over India using CMIP6 models during JJAS season.

  • A generalized extreme values distribution is utilized to estimate the climate extreme.

  • Four socioeconomic paths are used as SSP126, SSP245, SSP370, and SSP585 to understand the impact of low-emission to high-emission scenarios over precipitation.

  • A strong association is observed between surface air temperature and precipitation over India.

  • Extreme precipitation may rise up to 80% over India in the SSP585 scenario.

The Indian monsoon season, a key element of the world's climate system, supplies water to South Asia's heavily populated areas. During the summer, almost 80% of India's yearly precipitation falls, supplying water to the crops during the peak of the agricultural year. India is one of the most vulnerable countries because a significant portion of its population depends on climate-sensitive industries. The variability of mean and extreme climate changes is essential for civilization and farming (Katz & Brown 1992). This is due to the fact that societal (Stenseth 2002; Alexander & Perkins 2013; Hsiang et al. 2013; Seddon et al. 2016) and ecosystem impacts scale with climate variability, which further causes an increase in extreme events (Schär et al. 2004). This situation is expected to worsen as global temperatures rise (Krishnan et al. 2020). Consequently, the ISM is linked to human health and socioeconomic status of people. The persistent and unmanaged increase in global temperature, which is primarily caused by human activity, will result in severe and deadly extremes, including scorching heatwaves, widespread droughts, flooding, storms and other factors (Hirabayashi et al. 2013; Lee & Marotzke 2021; IPCC 2022). It is essential to understand historical changes and the driving forces behind them in order to understand the forthcoming predictions concerning potential alterations in monsoon precipitation. To properly understand this content, external and internal drivers must be distinguished. During the 20th century, anthropogenic forcings and natural forcings competed with each other to influence changes in monsoon rainfall on multi-millennial scales of paleoclimate and Green House Gases have been dominant as an external forcing since the early 21st century (Wang et al. 2005; Ming et al. 2020). According to multi-millennial paleo-records, pronounced variations have been observed in the summer monsoons of East Asia and India.

The primary cause of the paleoclimatic variations in monsoon rainfall, according to numerous studies (Wang et al. 2005, 2008; Zhang et al. 2008, 2019; Ming et al. 2020), is variations in solar radiation in the Northern Hemisphere, which affects the location of the Intertropical Convergence Zone because of changes in orbital forcing. Gradual changes in insolation are insufficient, particularly to account for abrupt nonlinear monsoon transitions like those seen in the Tibetan Plateau during the Holocene which suggests that internal feedback processes are in action (Herzschuh et al. 2014; Boos & Korty 2016; Wang et al. 2020).

An internal mechanism defined as the moisture-advection feedback (Levermann et al. 2009), that can produce sudden transitions, may have been the source of the rapid changes that occurred on the Tibetan Plateau during the Holocene, according to Herzschuh et al. (2014). Water vapor and cloud feedback might have had additional amplifying effects, according to Jalihal et al. (2019). Ramanathan et al. 2005; Bollasina et al. 2011; Shah & Mishra 2016; Jin & Wang (2017) have all reported diminishing rainfall in central India in the ISM season throughout the second half of the 20th century.

These prolonged trends are dominated by conflicting impacts of external human-induced influences, primarily the effect of Green House Gases, followed by the effects of sulfate aerosols and land-surface changes (Singh et al. 2019). This is because orbital forcing, which was once dominant, is now a minor component of external forcing in the current century.

The latest studies using global coupled models show that as a result of climate change, the Indian monsoon will experience more rainfall during the 21st century (Chaturvedi et al. 2012; Menon et al. 2013; Lee & Wang 2014; Asharaf & Ahrens 2015; Mie et al. 2015; Sharmila et al. 2015; Varghese et al. 2020; Salunke et al. 2023).

This tendency has also been observed in the multi-model average (Menon et al. 2013), the mean top-performing four models (Lee & Wang 2014), the average of the best deep convection scheme (Varghese et al. 2020), and other models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). According to Menon et al. 2013, the Indian monsoon rainfall for Representative Concentration Pathway 8.5 (RCP8.5) is predicted by CMIP5 models to increase rainfall by 2.3% K − 1.

The evaluation of the ten best models for mean and extreme precipitation of CMIP6 models has been studied by Kushwaha et al. 2024. As a result, it will be crucial to develop future strategies for agricultural policy and water resources management once we better understand the ISM's responsiveness and variability to various global warming scenarios. Understanding and predicting the characteristics of the climate depend mainly on global climate models (GCMs). Gusain et al. (2020) had previously drawn attention to the observed inconsistency across models in enhancing their geographical depiction of the ISM. Despite enormous gains, GCMs frequently struggle to simulate local and regional effects. Before, it was difficult to conduct the regional effects analyses due to regional climate characteristics (Rana et al. 2020). The daily precipitation that climate models simulate has not been sufficiently addressed in the literature. However, CMIP5 (Kumar et al. 2013; Liu et al. 2014; Mehran et al. 2014; Khayyun et al. 2020) and CMIP6 outputs (Rivera & Arnould 2020) have been utilized to extensively assess monthly and annual precipitation. Therefore, we utilize daily precipitation data from CMIP6 and see the projection of climate change.

Regional Indian models reported the occurrence of substantial biases in their simulations for precipitation due to model uncertainty (Jain et al. 2019; Kushwaha & Pandy 2022). These substantial biases observed in global models are frequently attributed to incomplete resolution of physical processes, coarse resolutions, and oversimplified parameterization of intricate climate-relevant systems (Wei & Qiao 2017). It is unknown how they will alter in the future or whether they will still be relevant to climate change at the regional level (Christensen et al. 2013; Xie et al. 2015). The reliability of results derived on single models or multi-model mean of CMIP5 projections is not adequate to offer reliable future projection, especially at the regional levels (Menon et al. 2013; Sharmila et al. 2015; Moon & Ha 2020; Salunke et al. 2023). Therefore, it is essential to take the most recent Coupled Model Intercomparison Project Phase 6 (CMIP6) models with consideration of the four different Shared Socioeconomic Pathways (SSPs) for the reliable projection of the mean and extreme precipitations on regional scale. The ability to capture the Western Ghats' rainfall pattern has also increased and is consistent with Gusain et al. (2020). According to the CMIP6 models, the ISM would intensify significantly with climate change. In addition, we computed the average multi-model spatial patterns of the anticipated change in mean and extreme precipitation by the end of the 21st century. Many modeling centers provide many models and some of those depend on overlapping model elements; therefore, it is impossible to see the models as independent of each other (Knutti et al. 2017). Results must be in accordance with this context. Compared to CMIP5, the average multi-model trend identified in CMIP6 with an increase of 24.3% until 2100 appears stronger (Chaturvedi et al. 2012; Menon et al. 2013).

In this study, 15 CMIP6 projections of mean precipitation and extreme precipitation using generalized extreme value (GEV) distribution during the monsoon season (JJAS) over India from the near future (2021–2040), mid future (2041–2060) and far future (2081–2100) to present under the four socioeconomic paths are used as SSP126, SSP245, SSP370 and SSP585. In addition, the CMIP6 projection of surface air temperature during JJAS is also determined over India for a similar period. Furthermore, a teleconnection pattern is also established between surface air temperature and precipitation. A brief description of the model data and methods is given in Section 2, CMIP6 model data in Section 2.1, methodology in Section 2.2, and extreme value analysis in sub section 2.3. Section 3.1 evaluates how well the model can reproduce climatology and variability patterns of precipitation and air temperature during JJAS for the near future, mid future and far future. In Section 3.2, the findings of multi-model projection of precipitation, in Section 3.3 multi-model projection of surface air temperature and in Section 3.4 teleconnection between precipitation and air temperature. Section 4 describes the summary and conclusion.

CMIP6 models data

The monthly data used in this analysis for the period 1995–2014 is derived from historical simulations of 15 CMIP6 models that were chosen (Erying 2016). The monthly projection data of precipitation and surface air temperature under four SSPs (SSP126, SSP245, SSP370 and SSP585), the low to high emissions scenarios (2081–2100) are used (O'Neill et al. 2016). The provided ESGF link (https://esgf-node.llnl.gov/search/cmip6) allows access to these datasets.

The study area lies between latitudes 8°N–37°N and longitudes 68°E–97°E, north of the equator. Table 1 offers a summary of the model names, modeling centers and data sources used in this study. Multi-model ensemble means (MMM) are derived from model simple average of each model, and multi-model ensemble extremes (MME) are determined by using the GEV distribution for each model. The seasonal data is estimated by using a climate data operator (CDO) for extracting the June to September data from monthly data.

Table 1

List of CMIP6 models

Sr. No.Model nameResolution (latitude × longitude)Country/Source
1. ACCESS-ESM1-5 1.2° × 1.9° Australia 
2. CanESM5 2.8° × 2.8° Canada 
3. CESM2-WACCM 0.9° × 1.3° USA 
4. CMCC-CM2-SR5 0.9° × 1.3° Italy 
5. EC-Earth3-Veg 0.7° × 0.7° Europe 
6. FGOALS-g3 2.3° × 2.0° China 
7. IITM-ESM 1.9° × 1.9° India 
8. INM-CM4-8 1.5° × 2.0° Russia 
9. INM-CM5-0 1.5° × 2.0° Russia 
10. IPSL-CM6A-LR 1.3° × 2.5° France 
11. KACE-1-0-G 1.3° × 1.9° Korea 
12. MIROC6 1.4° × 1.4° Japan 
13. MPI-ESM1-2-LR 1.9° × 1.9° Germany 
14. MRI-ESM2-0 1.1° × 1.1° Japan 
15. NorESM2-MM 0.9° × 1.3° Norway 
Sr. No.Model nameResolution (latitude × longitude)Country/Source
1. ACCESS-ESM1-5 1.2° × 1.9° Australia 
2. CanESM5 2.8° × 2.8° Canada 
3. CESM2-WACCM 0.9° × 1.3° USA 
4. CMCC-CM2-SR5 0.9° × 1.3° Italy 
5. EC-Earth3-Veg 0.7° × 0.7° Europe 
6. FGOALS-g3 2.3° × 2.0° China 
7. IITM-ESM 1.9° × 1.9° India 
8. INM-CM4-8 1.5° × 2.0° Russia 
9. INM-CM5-0 1.5° × 2.0° Russia 
10. IPSL-CM6A-LR 1.3° × 2.5° France 
11. KACE-1-0-G 1.3° × 1.9° Korea 
12. MIROC6 1.4° × 1.4° Japan 
13. MPI-ESM1-2-LR 1.9° × 1.9° Germany 
14. MRI-ESM2-0 1.1° × 1.1° Japan 
15. NorESM2-MM 0.9° × 1.3° Norway 

Methodology

This study focuses on the air temperature and precipitation teleconnections and their mean and extreme variability during the ISM season (JJAS). Several crucial methodological procedures must be followed in order to implement the use of CMIP6 analysis. The goal of CMIP6 is to improve our comprehension of variations in climate through global collaboration amongst climate modeling organizations. Obtaining CMIP6 data from approved archives, including the Earth System Grid Federation (ESGF), is the first stage. Considering this as our goal, we choose pertinent variables from the CMIP6 datasets, such as air temperature and precipitation. After that, we use data analysis and processing. To fit the spatial and temporal scales of our study region, this may entail downscaling, aggregation, or interpolation of data. Next, by contrasting model outputs with observed data sets, we evaluate how well the CMIP6 models replicated past climate conditions. Using CMIP6 predictions, we examine future variations in air temperature and precipitation under the various greenhouse gas (GHG) emission scenarios (SSP124, SSP245, SSP370, and SSP585). To evaluate the spectrum of possible future climatic outcomes, we analyze model uncertainty and ensemble means. We analyze the regional distribution of anticipated variations in precipitation and air temperature to determine vulnerable locations and possible effects on human societies, agriculture, water resources, ecosystems and agriculture. The statistical significance of each grid point difference between the scenario and the historical period is determined using a multi-model ensemble. Climatology, variability (standard deviation), and projection changes (Future–Historical) are calculated for air temperature and precipitation. The mean and extreme are the main findings and the difference is calculated by spatial distribution of all 15 multi-model ensembles. We point out that only models that produce data in both experiments, namely the historical simulation and the SSP scenario, are used in the difference and significance calculations. Land regions (i.e., the SSP scenarios or historical simulations) where the MMM exhibits a strong teleconnection in at least one trial are examined. In this instance, significant regional teleconnections are found whenever the multimodel ensemble mean and extreme for temperature and precipitation exhibit a teleconnection that is regarded significantly with linear regression using a scatter plot.

Extreme value analysis

Extreme Value Theory (EVT) is widely used to simulate extreme cases of genuine climate occurrences (Coles 2001; Kharin & Zwiers 2005; Min et al. 2013). One benefit of using EVT is its capacity to function with all the characteristics of extreme occurrences, including frequency, intensity, and volatility. The GEV distribution is one of the primary techniques used by EVT, while generalized Pareto distribution (GPD) is an alternative approach to fit climate extreme values. The GPD is used to fit data beyond the threshold in the peaks over threshold (POT) framework. The two main challenges for threshold based GPD are threshold selection and parameter estimation. The block maxima of independently distributed random variables with the same distribution comprise the GEV distribution (Coles 2001). As a result, the GEV distribution can better approach the seasonal extreme values than other distributions. On the other hand, the GEV distribution is the largest block distribution of randomly distributed variables with the same distribution (Coles 2001). The Fisher & Trippett (1928) limiting theorem states that the three limiting distributions (Gumbel, Fréchet, and Weibull) can be united into one single entity known as the GEV distribution. The GEV distribution fits inter-annual year samples (x) of daily precipitation and air temperature, and the cumulative density function (cdf) of the distribution is represented as follows:
formula
(1)
where and μ are the location, scale, and shape parameters respectively. Location parameter demonstrates the GEV distribution's proximity to the center, which is equivalent to the mean of a normal distribution. Hence, the location parameter is used to display the climatology of extremes. The scale parameter of the GEV distribution gives the measurement of width and is equivalent to the standard deviation of normal distribution, hence is used as the variability of extreme precipitation and air temperature. Additionally, depending on the sign of the shape parameter, the Gumbel, Fréchet, and Weibull distributions are represented by , and respectively (Kumar et al. 2024).

Climatology and variability

The performance of 15 multi-model ensemble spatial distributions of mean and extreme precipitation under four different emission scenarios (SSP126, SSP245, SSP370 and SSP585) during the ISM (JJAS) season for near future (2021–2040), mid future (2021–2040), and far future (2081–2100) are shown in Figure 1. The mean precipitation has the lowest values in the southeast (∼9 mm/day), northeast (∼12 mm/day), and western ghats ∼10 mm/day, <∼ 7 mm/day except these regions of India under the SSP126 in near future (NF), mid future (MF) and far future (FF). Moist air can be forced upward by mountains, resulting in a rain shadow effect on the leeward side and orographic rainfall on the windward side.
Figure 1

Spatial distribution of the climatology mean and extreme precipitation (mm/day) for four future scenarios SSP126, SSP245, SSP370, and SSP585 for near future (NF) during 2021–2040, mid future (MF) during 2041–2060, far future (FF) during 2081–2100.

Figure 1

Spatial distribution of the climatology mean and extreme precipitation (mm/day) for four future scenarios SSP126, SSP245, SSP370, and SSP585 for near future (NF) during 2021–2040, mid future (MF) during 2041–2060, far future (FF) during 2081–2100.

Close modal

The SSP245 scenario shows the mean precipitation increasing in the northeastern and eastern regions by 9–11 mm/day, in the northern part by 4–5 mm/day, and in the southwest peninsula by 8–10 mm/day, while NF, MF and FF show the increasing precipitation variability. A moderate level of climate change mitigation is assumed in the SSP245 scenario during NF, MF and FF. In the SSP370 scenario, the precipitation has a moderate increase in the eastern and northeastern regions (10–11 mm/day) during NF, 11–12 mm/day during MF, 12–14 mm/day during FF, in the northern part 3–5 mm/day and near the southwest coast is 9–11 mm/day. Precipitation patterns may vary due to these changes, favoring higher rainfall in some areas, such as the northeastern and eastern parts of India and the areas close to the southwest coast. The SSP585 shows a strong intensity of precipitation with an amplitude of 13–15 mm/day in the northeast and western ghats, and 4–6 mm/day in the northern region during ISM season (JJAS) in NF 2021–2040, in MF 2041–2060, in FF 2081–2100. The SSP585 scenario anticipates very high GHG emissions and insufficient actions to address climate change. Overall, the precipitation trend for all four possible SSP scenarios from 2081 to 2100 is similar with increasing intensity with an increase of low to high emissions. The distribution and intensity of precipitation may change in atmospheric circulation patterns brought on by higher GHG concentrations.

The extreme precipitation under SSP585 shows the highest precipitation with respect to other scenarios in the southeast (40–48 mm/day) and northeast (42–50 mm/day) and in the peninsula of the southwest region of India, 48 mm/day by the end of the 21st century (2081–2100) and in MF (2041–2060). Additionally, the models predict the precipitation would increase over eastern and central India throughout the monsoon season and over peninsular India after the monsoon months (Salunke et al. 2023). Extreme precipitation events may occur more frequently as a result of these changes in several areas, including India's southeast, northeast, and southwest. In the SSP585 scenario, precipitation shows a strong increasing pattern in the northeastern, eastern, southern, and some parts of the western regions (38–40 mm/day), while in the northern region it is 16–19 mm/day in MF and in FF.

Several events under the SSP370 scenario could cause exceptionally heavy precipitation. The SSP370 scenario may have less precipitation in the northern region (13–19 mm/day) and very high precipitation in the northeastern, eastern, central, southern, and western regions (31–35 mm/day) in NF, while in MF 32–40 mm/day, 36–44 mm/day in FF. For example, alterations in atmospheric circulation patterns, such as those affecting the ISM or other regional monsoons, may cause more intense and protracted rainfall events in these locations. The least intensity SSP126 scenario shows less precipitation in the northern region, whereas the eastern and northeastern regions show an increasing pattern of precipitation, approximately 31–35 mm/day, while in MF 32–40 mm/day, 36–44 mm/day in FF. Near the surface, adiabatic warmth results from air sinking brought on by an upper atmospheric high over North India (Dubey et al. 2021). Reduced precipitation in the north may result from various sources, including modifications to large-scale atmospheric circulation systems like the jet stream, modifications to storm trajectories, or adjustments to local temperature gradients. The SSP245 scenario shows higher precipitation (>44 mm/day) in India's eastern, northeastern, and Himalayan regions and less than 19 mm/day precipitation mainly in the northern region of India.

Numerous reasons could cause these places to receive more precipitation under the SSP245 scenario. For instance, the Himalayan region has complicated topography that can promote orographic precipitation, in which humid air is forced to climb and cool, increasing rainfall on windward slopes. Additionally, variations in atmospheric circulation patterns, such as the ISM, can be a major factor in shaping rainfall patterns in these areas. Currently, less rain is falling in the Indo-Gangetic area due to a weakening of the mid-tropospheric cyclone occurring over East India caused by the elevated surface pressure over Tibet (Singh et al. 2022). The topography of a place, such as its mountains or coastline characteristics, can also affect precipitation patterns due to orographic effects and interactions with air masses that are high in moisture. The distribution and intensity of precipitation may change because of variations in atmospheric circulation patterns brought on by higher GHG concentrations.

In Figure 2, the variability of 15 multi-model ensemble means and extreme precipitation is shown during the ISM (JJAS) season under four different scenarios (SSP126, SSP245, SSP370, and SPP585) during the near future (2021–2040), mid future (2041–2060) and the end of the century (2081–2100). The variability of mean precipitation has a moderate value in the southeast (9–10 mm/day), northeast (11–12 mm/day), and peninsula of the southwest (9–11 mm/day) regions of India under the SSP126. The variability of precipitation shows a similar increasing pattern of precipitation in NF, MF and FF. The seasonal monsoon has a major impact on India's southeast, northeast, and southwest areas. These regions receive a significant percentage of their annual precipitation from the monsoon, which makes the precipitation there more regular throughout the monsoon season and contributes to the moderate variability in mean precipitation levels.
Figure 2

Spatial distribution of variability of mean and extreme precipitation (mm/day) for four future emission scenarios SSP126, SSP245, SSP370, and SSP585 for near future (NF) during 2021–2040, mid future (MF) during 2041–2060, far future (FF) during 2081–2100.

Figure 2

Spatial distribution of variability of mean and extreme precipitation (mm/day) for four future emission scenarios SSP126, SSP245, SSP370, and SSP585 for near future (NF) during 2021–2040, mid future (MF) during 2041–2060, far future (FF) during 2081–2100.

Close modal

In scenario SSP245, mean precipitation has a strong increase in the northeastern region at 10 mm/day, the eastern region at 12 mm/day and over the Himalayas and peninsula of the southwest at 9 mm/day. The seasonal monsoon has an enormous effect on India's southeast, northeast and southwest areas. These places receive a significant percentage of their annual precipitation from the monsoon, which causes more constant precipitation throughout the monsoon season and contributes to moderate variability in mean precipitation levels. Scenarios SSP370 and SSP585 during NF, MF and FF show a similar pattern of mean precipitation that may have moderated in the northern region (<7 mm/day), and except for the northern region, it is a robust increase in the ISM season with the same time period of 2081–2100. In India's northern region during the summer monsoon season, both SSP370 and SSP585 exhibit a trend of moderate mean precipitation. This could mean that, from 2081 to 2100, rainfall in this location will decline or moderately increase under both scenarios.

The extreme precipitation variability, SSP585, shows a strong rise in precipitation in the whole Indian region of 11–16 mm/day, except that the region of Ladakh and some northern parts of Rajasthan have lower precipitation of 7 mm/day. The scenarios SSP126, SSP245, and SSP370 show a similar pattern with less extreme precipitation value. Both scenarios predict a significant increase in mean precipitation throughout the ISM season across the Indian subcontinent, with the exception of the northern region. This implies that the models forecast a notable increase in rainfall in India's regions over the same time period in the ISM season during 2021–2040, 2041–2060, and 2081–2100. It's crucial to realize that these scenarios are probable future routes rather than forecasts, depending on various hypotheses regarding world development, population and climate policies. The actual results of climate projections and models may change depending on a number of factors. This result indicates that mean and extreme precipitation are strongly associated with each other.

Multimodel projection of precipitation

The distribution of future projections of 15 multi-model ensembles for mean and extreme ISM (JJAS) season for four different scenarios of 20 years with three different time periods during 2021–2040, 2041–2060, 2081–2100 relative to the historical timeframe 1995–2014 in Figure 3. In the high-emission scenario (SSP585), a pronounced increase in mean precipitation is observed in major parts of India, including a 60–70% increase in the western part. In central India, a notable increase in the frequency of localized heavy precipitation events is attributed in part to changes in moisture availability brought on by GHG-induced warming, atmospheric stability, aerosols, and growing urbanization (Krishnan et al. 2020). The anticipated increase in summer monsoon precipitation and the projected long-term rise in variability may result in a greater frequency of exceptionally wet years and potentially more high-rainfall events (Turner & Slingo 2009; Sharmila et al. 2015). While high-precipitation events in other growing states can hurt the plants, crops still need water, especially during the first growing period (Revadekar & Preethi 2012). In SSP370, a 20–60% increasing pattern of mean precipitation in the northeastern, eastern, western and southern regions during NF, MF, and FF of India is shown. The monsoon has a tremendous impact on India, and any changes to its patterns can have a big impact on local precipitation. Warmer temperatures can alter the onset, intensity, and withdrawal of the monsoon. The projected changes under SSP126 show mean precipitation in Rajasthan (northern region) at 35% in the northeast and eastern part considered at 21%, and the peninsula of southwest India at 19% region of India during NF and MF. Under the mild emission scenario (SSP245), a similar pattern to SSP126 of mean precipitation with an increasing value of precipitation in the ISM season is observed. Climatic projections are vulnerable to a variety of uncertainties, including the difficulty of precisely estimating future GHG emissions and the complexity of climatic processes. The results of different scenarios can vary depending on these uncertainties.
Figure 3

Spatial distribution of projection change (future–historical) in mean and extreme precipitation (mm/day) for four future scenarios SSP126, SSP245, SSP370, and SSP585 for near future (NF) during 2021–2040, mid future (MF) during 2041–2060, far future (FF) during 2081–2100 with historical data from 1995 to 2014.

Figure 3

Spatial distribution of projection change (future–historical) in mean and extreme precipitation (mm/day) for four future scenarios SSP126, SSP245, SSP370, and SSP585 for near future (NF) during 2021–2040, mid future (MF) during 2041–2060, far future (FF) during 2081–2100 with historical data from 1995 to 2014.

Close modal

The extreme scenario SSP585 shows strong and higher precipitation greater than 80% mm/day in the western region of India, showing a flood condition, while Ladakh shows drought conditions. Extreme precipitation is more common in India's urban areas, which may result from urbanization and increasing GHGs warming. Due to increased warming, the frequency of extreme precipitation events may rise throughout India, with the central and southern regions being more affected. The SSP370 scenario shows an increasing pattern of extreme precipitation in the whole Indian region, except for a few areas. Projection of extreme precipitation pattern is similar in NF and MF while in FF the pattern changes drastically toward the western side of India. These modifications may have an impact on how moist air masses move, altering precipitation patterns and perhaps causing more severe rainfall in some areas.

Since SSP245 is connected to a pathway with moderate emissions, GHG emissions that cause global warming are still present. Even though the scenario's emissions might be less than those of more extreme routes, they nevertheless have the potential to alter climate trends. For SSP245, the extreme precipitation is increased by 25% in the western, while the northeastern and southern regions of India are considered less than 20% in the ISM season. The SSP126 scenario shows 5–10% extreme precipitation across the Indian subcontinent. Indian monsoon dynamics can still be impacted by changes in temperature and atmospheric patterns, even though SSP126 indicates a more sustainable future. Variations in monsoon behavior and, as a result, precipitation patterns may result from these changes.

Multimodel projection of surface air temperature

According to historical data from 1995 to 2014, Figure 4 shows the regional distribution of the climatology mean and extreme air temperature for 15 multi-model ensembles throughout the ISM (JJAS) season under four different scenarios: SSP126, SSP245, SSP370 and SSP585 for the near future timeframe from 2021 to 2040, for mid future timeframe from 2041 to 2060 and for the far future timeframe from 2080 to 2100. For climatology mean air temperature, SSP126 shows a higher air temperature value in the middle area of region 33–36 °C in NF and increasing air temperature pattern in MF and FF owing to the urban heat island effect, which may cause this region to experience warmer temperatures than the nearby rural areas and may also be a result of factors like urbanization and industry. The southern part of the Indian region shows a temperature of 24–30 °C due to the effect of the ocean, which helps control temperature variations, coastal regions typically experience more temperate temperatures and upper side of the Indian region, Himalayan region shows similar pattern of a temperature of 12–21 °C in NF, MF and FF has a colder climate, probably due to its altitude and proximity to the cold air masses from the north. In the SSP245 scenario, air temperature decreases from the northern to the eastern part of India around 36–33 °C and in the Ladakh region is considered to be less than 6 °C and in western, southern, and northeastern regions is considered less than 30 °C due to their proximity to the the Bay of Bengal (south and northeastern parts) and Arabian Sea (western region), places which may have a marine climate effect. Temperatures have a tendency to be moderated by the presence of water bodies, resulting in comparatively cooler conditions.
Figure 4

Spatial distribution of the climatology means and extreme of air temperature (°C) for four future scenarios SSP126, SSP245, SSP370, and SSP585 for near future (NF) during 2021–2040, mid future (MF) during 2041–2060, far future (FF) during 2081–2100 with historical data from 1995 to 2014.

Figure 4

Spatial distribution of the climatology means and extreme of air temperature (°C) for four future scenarios SSP126, SSP245, SSP370, and SSP585 for near future (NF) during 2021–2040, mid future (MF) during 2041–2060, far future (FF) during 2081–2100 with historical data from 1995 to 2014.

Close modal

The mountainous terrain that distinguishes the northeastern region of India might also contribute to significantly cooler temperatures. SSP370 shows a pronounced enhancement in air temperature in the northern part at 39 °C and in the southern part at 27 °C while less than 6 °C air temperature in Ladakh and Arunachal Pradesh because the quantity of solar radiation that is reflected or absorbed depends on the Earth's surface's reflectance. Ladakh and other snow-covered areas typically reflect more sunlight, which results in lower temperatures. However, the SSP585 extreme scenario shows increasingly stronger temperatures in the northern region of India while in southern, western, and eastern regions it is 30–35 °C during the ISM season (JJAS). The NF, MF and FF show mean air temperature increase in a similar pattern in SSP126, SSP245, SSP370 and SSP585, which is a significant result for projection.

Under the SSP585 extreme scenario, the interaction of polar amplification, modifications in air circulation, and the effect of ocean warming can lead to rising temperatures and more extreme weather events in different parts of India. The northern region may suffer more rapid warming, whilst the southern, western, and eastern regions may have higher temperatures and a greater danger of heat waves during the ISM season. It is critical to remember that projections based on assumptions, such as SSP585, are what climate scenarios like SSP585 are, and that a variety of uncertainties and prospective climate change mitigation measures may affect the outcomes of these scenarios. The SSP126 scenario shows extreme air temperature in the northern region at 36 °C while southern and eastern at 33 °C, in western at 27 °C and Ladakh at 9 °C. SSP245, SSP370, and SSP585 show extreme temperatures in the region of the south part of India 22–30 °C and the upper side of the Indian region and Himalayan region shows temperatures of 12–24 °C during the ISM season (JJAS). The climatology of extreme air temperature is like a mean air temperature pattern, but here is a 2 °C increment of air temperature. According to various paths for increasing GHG concentrations (namely RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5), various climate change scenarios, like those in IPCC reports, frequently depict probable future rises in the world average temperature. These projections show probable shifts in the global mean temperature in comparison to a pre-industrial baseline, such as a 1.5 °C or 2 °C increase over pre-industrial levels.

A crucial factor in determining the trend in rainfall is the variability of the 15 multi-model ensembles used to simulate air temperature anomalies (SD). Relative to the historical period of 1995–2014, Figure 5 shows the mean and extreme air temperature variations during the ISM (JJAS) season for four alternative scenarios throughout the three different years 2021–2040, 2041–2060 and 2081–2100. SSP126, SSP245, SSP370 and SSP585 in the near future show a moderate mean air temperature of 1–1.6 °C in the middle of the region for the analysis of mean air temperature variability due to the amount of GHG emissions and their atmospheric concentration will affect how much global warming occurs, and as a result, how much regional temperature changes. SSP245 shows an air temperature in the range of 1.2–1.8 °C. There will always be uncertainty surrounding climate projections, and actual temperature changes throughout the ISM season will depend on intricate interactions among numerous factors. During the ISM season (JJAS), the SSP370 scenario shows mean air temperature may have 0.45–0.55 °C variability (SD) which is a lower standard deviation denoting more stability or consistency around the mean temperature, while a higher standard deviation denotes greater fluctuation. In the SSP585 scenario, there is variability over northern 2.2 °C, central 2.4 °C parts of India. Temperatures in that area could vary from about 27.6–32.4 °C relative to the reference period mean due to the fluctuation of 2.4 °C over central India.
Figure 5

Spatial distribution of variability of mean and extreme of air temperature (°C) for four future scenarios SSP126, SSP245, SSP370, and SSP585 for near future (NF) during 2021–2040, mid future (MF) during 2041–2060, far future (FF) during 2081–2100 with historical data from 1995 to 2014.

Figure 5

Spatial distribution of variability of mean and extreme of air temperature (°C) for four future scenarios SSP126, SSP245, SSP370, and SSP585 for near future (NF) during 2021–2040, mid future (MF) during 2041–2060, far future (FF) during 2081–2100 with historical data from 1995 to 2014.

Close modal

SSP126 is one of the SSPs that depicts a future with low GHG emissions in which significant efforts are made to reduce climate change and make the transition to a sustainable and low-carbon society. Extreme air temperature variability, SSP126 shows a strong increasing air temperature pattern over the central, northeastern, and eastern regions of 2.2–2.8 °C due to the amount of GHG emissions, local feedback mechanisms, changes in land use, and regional climate dynamics could all have an impact on the pattern of sharply rising air temperature in certain places, while in the southern and northern regions, it is less than 2 °C of standard deviation. The SSP245, SSP370, and SSP585 show similar patterns of extreme air temperature variability around 2.4 °C with decreasing extreme air temperature in Ladakh, eastern, southern, and some parts of western India during the ISM season (JJAS). The amount of GHG emissions, which varies throughout SSPs, will have an impact on global warming and contribute to extreme weather events.

Changes in atmospheric circulation patterns, including alterations in the size and location of the Indian monsoon, may influence temperature variations during the monsoon season. SSPs are feasible paths based on assumptions, not exact predictions of future climate outcomes. Climate forecasts will always be subject to uncertainty because actual temperature changes and extreme temperature patterns will be dependent on intricate interactions between numerous elements. This finding demonstrates that the air temperature values for the mean and extreme are likely to deviate from one another.

Figure 6 shows the future projection calculated using future minus historical air temperature from 15 multi-model ensembles mean and extreme during the ISM (JJAS) season for four different scenarios of 20 years during 2081–2100 relative to the historical period 1995–2014. Expected variations for all scenarios for mean and extreme show the intensity of air temperature, as shown in Figure 6. The SSP126 scenario depicted here shows a mean air temperature value of 1–1.3 °C in the south and 1.6–2.2 °C in the northeast and Ladakh region because temperature trends can also be influenced by a region's topographical characteristics, such as height and proximity to water bodies. For instance, colder climates can be found in higher elevations like Ladakh. According to the SSP245 scenario, air temperature intensities are rising in Ladakh at around 2.8 °C, above the central part of India at a rate of roughly 1.9–2.5 °C, and below the central part of India at a rate of less than 2.2 °C.
Figure 6

Spatial distribution of projection changes in mean and extreme air temperature (°C) for four future scenarios SSP126, SSP245, SSP370, and SSP585 for near future (NF) during 2021–2040, mid future (MF) during 2041–2060, far future (FF) during 2081–2100 with historical data from 1995 to 2014.

Figure 6

Spatial distribution of projection changes in mean and extreme air temperature (°C) for four future scenarios SSP126, SSP245, SSP370, and SSP585 for near future (NF) during 2021–2040, mid future (MF) during 2041–2060, far future (FF) during 2081–2100 with historical data from 1995 to 2014.

Close modal

The SSP245 scenario depicts a world where GHG emissions are projected to be moderately high. The amount of GHG emissions will be a major factor in determining how much the planet warms and how much regional temperature changes as a result. The SSP370 scenario depicts an increase in air temperature from top to bottom in India, from 3.5 °C to 2.2 °C. SSP585 indicates the mean air temperature in the upper north area of India, which ranges from 3.3 to 3.7 °C during the summer monsoon season. The widespread consensus that various GHG emission paths can result in varying degrees of temperature increase is supported by the areas. In comparison to lower emission scenarios, the higher emission scenarios (such as SSP370 and SSP585) are most likely to cause larger temperature changes. According to the expected changes for all extreme air temperature scenarios, SSP126, extreme air temperature values range from 2.8 to 3 °C in the southeast and from 2.5 °C in the northeast due to the climate of coastal places being strongly influenced by ocean currents.

Warm ocean currents can cause temperatures to rise, while cold currents can cause temperatures to fall. The temperature patterns in the southeast and northeast may fluctuate depending on the location and direction of the currents. In SSP245, the northeastern, central, and northern parts of India had extreme air temperatures varying between 2 and 3 °C, while the southern and western parts of India had declining severe air temperatures. The Bay of Bengal and the Arabian Sea are examples of big bodies of water close to coastal areas that can moderate temperatures and cause regional variances. The SSP370 scenario depicts rising extreme air temperatures in Ladakh and India's central and northeastern regions, while less extreme air temperatures of 2.5 °C are shown in the western section of the country due to local climatic patterns and temperature fluctuations may be affected by India's varied terrain, which includes mountainous regions like Ladakh and a variegated landscape in the country's central, northeastern, and western regions.

Wind patterns, altitude, and proximity to oceans or other major bodies of water can all have an impact. In the northern, upper part of central India, eastern, and some parts of the northeastern regions of India during the summer monsoon season, SSP585 exhibits an extreme air temperature pattern with a range between 3.5 and 5.5 °C. The average temperature in India has risen by approximately 0.2 °C in every scenario from the near future (2021–2040), mid future (2041–2060) and far future (2081–2100). In the scenarios from 2081 to 2100, the average temperature rose by around 0.2 °C, which is consistent with the general trend of global warming. As GHG concentrations continue to rise, temperature increases are expected to lead to higher global and regional averages. The forcing caused by changes in land use and land cover (LULC) and anthropogenic aerosols somewhat offset this temperature increase, which is primarily caused by GHG-induced warming.

Teleconnection between precipitation and air temperature

Figure 7 shows the teleconnection of precipitation and surface air temperature under four possible SSPs during the JJAS season. In this figure, scatter plots of the change in surface air temperature (future–historical) for the far future (2021–2040) are shown with respect to the changes in precipitation (future–historical) for SSP126, SSP245, SSP370 and SSP585. A strong negative association between air temperature and precipitation under SSP126 (r = −0.12), SSP245 (r = −0.48), SSP370 (r = −0.19) and SSP585(r = −0.13) has been established. Figure 8 shows the teleconnection of precipitation and surface air temperature under four possible SSPs during the JJAS season. In this figure, scatter plots of the change in surface air temperature (future–historical) for the far future (2041–2060) are shown with respect to the changes in precipitation (future–historical) for SSP126, SSP245, SSP370 and SSP585. A strong negative correlation association between air temperature and precipitation under SSP126 (r = 0.63), SSP245 (r = 0.12), SSP370 (r = −0.008) and SSP585 (r = 0.027) has been established. Furthermore, Figure 9 shows the teleconnection of surface air temperature and precipitation under four possible SSPs during the JJAS season. In this figure, scatter plots of the change in surface air temperature (future–historical) for the far future (2081–2100) are shown with respect to the changes in precipitation (future–historical) for SSP126, SSP245, SSP370 and SSP585. A strong association between air temperature and precipitation under SSP126 (r = 0.86) and SSP370 (r = 0.71) has been established. Furthermore, a moderate correlation between air temperature and precipitation is observed under SSP245 and SSP585 emissions scenarios.
Figure 7

Scatter plots between the projected changes mean precipitation and mean air temperature for the period near future (2021–2040) with respect to historical data from 1995 to 2014 for scenarios SSP126, SSP245, SSP370, SSP585 of CMIP6 15 models and their correlation.

Figure 7

Scatter plots between the projected changes mean precipitation and mean air temperature for the period near future (2021–2040) with respect to historical data from 1995 to 2014 for scenarios SSP126, SSP245, SSP370, SSP585 of CMIP6 15 models and their correlation.

Close modal
Figure 8

Scatter plots between the projected changes mean precipitation and mean air temperature for the period 2041–2060 mid future with respect to historical data from 1995 to 2014 for scenarios SSP126, SSP245, SSP370, SSP585 of CMIP6 15 models and their correlation.

Figure 8

Scatter plots between the projected changes mean precipitation and mean air temperature for the period 2041–2060 mid future with respect to historical data from 1995 to 2014 for scenarios SSP126, SSP245, SSP370, SSP585 of CMIP6 15 models and their correlation.

Close modal
Figure 9

Scatter plots between the projected changes mean precipitation and mean air temperature for the period 2081–2100 far future with respect to historical data from 1995 to 2014 for scenarios SSP126, SSP245, SSP370, SSP585 of CMIP6 15 models and their correlation.

Figure 9

Scatter plots between the projected changes mean precipitation and mean air temperature for the period 2081–2100 far future with respect to historical data from 1995 to 2014 for scenarios SSP126, SSP245, SSP370, SSP585 of CMIP6 15 models and their correlation.

Close modal

This study describes how the Indian subcontinent will warm broadly, with higher latitudes experiencing more intense warming. While the peninsula would suffer relatively less warming, the country's northwestern areas would face the most warming. Surface air temperature plays a significant role in intensifying the rainfall trends over the different regions of India. Like this, forecasts for future precipitation show that most of India's landmass is likely to experience a considerable rise in ISM season (JJAS) precipitation. Under all forcing scenarios, previous studies have indicated a rise in the expected air temperature intensity throughout the Indian regions for the foreseeable future and the end of the century but a decrease over the southern part of India (Katzenberger et al. 2021). Overall, there is an observed increasing trend in both air temperature and precipitation across emission scenarios, ranging from low (SSP126) to high (SSP585).

Our findings demonstrate how precipitation and surface air temperature are associated with various SSP scenarios in the CMIP6 models. The multi-model ensemble in CMIP6 is displayed with realistic patterns.

Forecasts were made using the models chosen, utilizing the products that best depict temperature and precipitation for the ISM season (JJAS). We examined the climatology, variability, and projected changes in the near future (2021–2040), mid future (2041–2060) and far future (2081–2100) during the ISM with respect to the historical temperature (1995–2014) during the ISM season. Therefore, CMIP6 models demonstrate the projected changes (2081–2100), and scatter plots demonstrate the strong association between precipitation and air temperature during JJAS months with the historical data (1995–2014). By the end of the 21st century, all models predicted a rise in mean summer monsoon precipitation under SSP585 and SSP370. According to the findings, India's mean and extreme precipitation are expected to shift significantly over the next few decades. The predictions illustrate the possibility of wetter conditions in the future by showing an overall increase in mean precipitation across most of the country. This might impact the availability of water resources, agriculture, and the overall hydrological balance in various areas.

The data also shows a rise in extreme precipitation occurrences, indicating a higher likelihood of intense and prolonged periods of downpours. Extreme precipitation patterns like this one might make floods more regular and severe, endangering infrastructure and way of life and complicating efforts to prepare for and manage disasters. According to SSP585, SSP370, SS245, and SSP126, a multi-model mean increase in precipitation of 14 mm/day, 8 mm/day, 4 mm/day, and 2 mm/day is anticipated, respectively. All scenarios during the near future, mid future and far future predict an increase in precipitation, particularly in the northern region, Himalayan region, western, southern, and northeastern Bay of Bengal and along India's west coast. Additionally, regardless of the SSPs, the simulated ensemble shows that precipitation is associated with global mean air temperature in the 21st century. The multi-model mean for JJAS predicts an increase of 22 mm/day and 2 °C of warming. With a maximum warming of 2–3 °C by the end of the current century, the temperature will rise consistently in the future throughout the ISM season.

The projection estimates indicate an upward tendency for the ISM season, with the most significant rise in precipitation being about 2 mm/day. Since the extreme precipitation and air temperature features and variability of rainfall and air temperature are beyond the study, they are still analyzed for high relevance, e.g., for drought and high-risk flooding events. Therefore, the predicted development could significantly impact India's agriculture and that of its neighbors to establish adaptation methods for the 21st century because the transition differs from the declining tendency in the second half of the 20th century. The study notes geographical differences in the anticipated changes, with certain regions of India anticipated to experience more significant changes in precipitation patterns than others. It highlights the significance of doing local-scale analyses and developing adaptation plans to deal with shifting precipitation's effects on various species and ecosystems. Additionally, the CMIP6 study recognizes inherent uncertainties in climate modeling. Precipitation projections are unpredictable in part because of variations in model performance and the complexity of atmospheric processes.

The novelty of climate projections is important in practical life for many sectors, including urban planning, agriculture, water resource management and disaster preparedness. This article explains how new insights from climate forecasts can be used to improve climate models, adaption plans, risk assessment and management, infrastructure planning, design and policy development. In conclusion, new developments in air temperature and precipitation projections improve the accuracy of climate models, inform adaptation plans, ease risk assessment and management, direct infrastructure development, and influence climate change policies. All of these benefits have been noticed in practical-world applications across a wide range of industries. Societies can better plan for and adapt to the challenges posed by a changing climate by incorporating new ideas into climate projections.

As a result, to create effective policies for climate resilience, decision-makers and policymakers should consider a wide variety of options by various models. The results of this CMIP6 analysis highlight the necessity of India's efforts in climate mitigation and adaptation. Changes in mean and extreme precipitation patterns are predicted. Increases in both mean and extreme precipitation variability may significantly impact changes in high-precipitation extremes, while rising mean temperatures may cause an increase in high-temperature extremes in the future. Studies using CMIP6 analysis can help to improve the robustness of climate projections, advance our understanding of the dynamics of climate change, and inform evidence-based decision-making for climate resilience and sustainable development by addressing these future-scope research directions.

The modeling organizations listed in Table 1 that provided the CMIP6 datasets are thanked by the authors. The data were contributed to CMIP6 by the World Climate Research Program (WCRP) working group on coupled modeling and made accessible through the ESGF repository of the Program for Climate Model Diagnosis and Intercomparison (https://pcmdi.llnl.gov/CMIP6/). P.K. acknowledges the DST Inspire Fellowship for financial support.

This research was financially supported by the first author (Prabha Kushwaha) from the Department of Science and Technology (DST) INSPIRE fellowship grant (IF170827).

All relevant data are available from an online repository or repositories.

The authors declare there is no conflict.

Alexander
L.
&
Perkins
S.
2013
Debate heating up over changes in climate variability
.
Environ. Res. Lett.
8
,
7
10,5
.
https://doi.org/10.1088/1748-9326/8/4/041001
.
Asharaf
S.
&
Ahrens
B.
2015
Indian summer monsoon rainfall processes in climate change scenarios
.
J. Clim.
28
,
5414
5429
.
https://doi.org/10.1175/JCLI-D-14-00233.1
.
Bollasina
M. A.
,
Ming
Y.
&
Ramaswamy
V.
2011
Anthropogenic aerosols and the weakening of the South Asian summer monsoon
.
Science
334
,
502
505
.
https://doi.org/10.1126/science.1204994
.
Boos
W. R.
&
Korty
R. L.
2016
Regional energy budget control of the intertropical convergence zone and application to mid-Holocene rainfall
.
Nat. Geosci.
9
,
892
897
.
https://doi.org/10.1038/ngeo2833
.
Chaturvedi
R. K.
,
Joshi
J.
,
Jayaraman
M.
,
Bala
G.
&
Ravindranath
N.
2012
Multi-model climate change projections for India under representative concentration pathways
.
Curr. Sci.
103
,
791
802
.
Christensen
J.
,
Kumar
K. K.
,
Aldrian
E.
,
An
S. I.
,
Cavalcanti
I.
,
Castro
M. d.
,
Dong
W.
,
Goswami
P.
,
Hall
A.
,
Kanyanga
J.
,
Kitoh
A.
,
Kossin
J.
,
Lau
N. C.
,
Renwick
J.
,
Stephenson
D.
,
Xie
S. P.
&
Zhou
T.
,
2013
Climate phenomena and their relevance for future regional climate change
. In:
Climate Change 2013 the Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change
(
Stocker
T. F.
,
Qin
D.
,
Plattner
G. K.
,
Tignor
M.
,
Allen
S. K.
,
Boschung
J.
,
Nauels
A.
,
Xia
Y.
,
Bex
V.
&
Midgley
P.
, eds).
Cambridge University Press
,
Cambridge
, pp.
1217
1308
.
Coles
S. G.
2001
An Introduction to Statistical Modeling of Extreme Values
.
Springer
,
London
, p
225
.
https://doi.org/10.1007/978-1-4471-3675-0
.
Dubey
A. K.
,
Kumar
P.
,
Saharwardi
M. S.
&
Javed
A.
2021
Understanding the hot season dynamics and variability across India
.
Weather Clim. Extremes
32
(
100317
),
2212
0947
.
https://doi.org/10.1016/j.wace.2021.100317
.
Gusain
A.
,
Ghosh
S.
&
Karmakar
S.
2020
Added value of CMIP6 over CMIP5 models in simulating Indian summer monsoon rainfall
.
Atmos. Res.
232
,
104680
.
https://doi.org/10.1016/j.atmosres.2019.104680
.
Herzschuh
U.
,
Borkowski
J.
,
Schewe
J.
,
Mischke
S.
&
Tian
F.
2014
Moisture-advection feedback supports strong early-to-mid Holocene monsoon climate on the eastern Tibetan Plateau as inferred from a pollen-based reconstruction
.
Palaeogeogr. Palaeocl.
402
,
44
54
.
https://doi.org/10.1016/j.palaeo.2014.02.022
.
Hirabayashi
Y.
,
Mahendran
R.
,
Koirala
S.
,
Konoshima
L.
,
Yamazaki
D.
&
Watanabe
S.
2013
Global flood risk under climate change
.
Nat. Clim. Change
3
,
816
821
.
doi:10.1038/nclimate1911
.
Hsiang
S. M.
,
Burke
M.
&
Miguel
E.
2013
Quantifying the influence of climate on human conflict
.
Science
341
,
1235 367–1235 367, 25. https://doi.org/10.1126/science.1235367
.
IPCC
2022
Climate change 2022: Impacts, adaptation, and vulnerability
. In:
Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change
(
Pörtner
H. O.
,
Roberts
D. C.
,
Tignor
M.
,
Poloczanska
E. S.
,
Mintenbeck
K.
&
Alegría
A.
, eds).
Cambridge University Press
,
Cambridge; New York, NY
, p.
3,056
.
Jain
S.
,
Salunke
P.
,
Mishra
S. K.
&
Sahany
S.
2019
Performance of CMIP5 models in the simulation of Indian summer monsoon
.
Theor. Appl. Climatol.
137
,
1429
1447
.
doi:10.1007/s00704-018-2674-3
.
Jalihal
C.
,
Srinivasan
J.
&
Chakraborty
A.
2019
Modulation of Indian monsoon by water vapor and cloud feedback over the past 22,000 years
.
Nat. Commun.
10
,
1
8
.
https://doi.org/10.1038/s41467-019-13754-6
.
Jin
Q.
&
Wang
C.
2017
A revival of Indian summer monsoon rainfall since 2002
.
Nat. Clim. Change
7
,
587
594
.
https://doi.org/10.1038/nclimate3348
.
Katz
R. W.
&
Brown
B. G.
1992
Extreme events in a changing climate: Variability is more important than averages
.
Clim. Change
21
,
289
302
.
https://doi.org/10.1007/BF00139728
.
Katzenberger
A.
,
Schewe
J.
,
Pongratz
J.
&
Levermann
A.
2021
Robust increase of Indian monsoon rainfall and Its variability under future warming in CMIP6 models
.
Earth Syst. Dyn.
12
,
367
386
.
https://doi.org/10.5194/esd-12-367
.
Kharin
V. V.
&
Zwiers
F. W.
2005
Estimating extremes in transient climate change simulations
.
J. Clim.
18
(
8
),
1156
1173
.
https://doi.org/10.1175/JCLI3320.1
.
Khayyun
T. S.
,
Alwan
I. A.
&
Hayder
A. M.
2020
Selection of suitable precipitation CMIP-5 sets of GCMs for Iraq using a symmetrical uncertainty filter
.
Mater. Sci. Eng.
13
.
https://doi.org/10.1088/1757-899x/671/1/012013
Knutti
R.
,
Sedlacek
J.
,
Sanderson
B. M.
,
Lorenz
R.
,
Fischer
E. M.
&
Erying
V.
2017
A climate model projection weighting scheme accounting for performance and interdependence
.
Geophys. Res. Lett.
44
,
1909
1918
.
https://doi.org/10.1002/2016GL072012
.
Krishnan
R.
,
Sanjay
J.
,
Gnanaseelan
C.
,
Mujumdar
M.
,
Kulkarni
A.
&
Chakraborty
S.
2020
Assessment of Climate Change Over the Indian Region: A Report of the Ministry of Earth Sciences (MOES)
, Vol.
226
.
Springer Nature
,
Berlin
.
Government of India. doi:10.1007/978-981-15-4327-2
.
Kumar
S.
,
Merwade
V.
,
Kinter
J. L.
&
Niyogi
D.
2013
Evaluation of temperature and precipitation trends and long-term persistence in CMIP5 twentieth-century climate simulations
.
J. Clim.
26
(
12
),
4168
4185
.
https://doi.org/10.1175/JCLI-D-12-00259.1
.
Kumar
P.
,
Yadav
A.
,
Sardana
D.
&
Prasad
R.
2024
Extreme wave height response to climate modes and its association with tropical cyclones over the Indo-Pacific Ocean
.
Ocean Eng.
296
,
116789
.
https://doi.org/10.1016/j.oceaneng.2024.116789
.
Kushwaha
P.
,
Pandey
V. K.
,
Kumar
P.
&
Sardana
D.
2024
CMIP6 model evaluation for mean and extreme precipitation over India
.
Pure Appl. Geophys.
181
,
655
678
.
https://doi.org/10.1007/s00024-023-03409-5
.
Lee
J. Y.
&
Marotzke
J.
2021
Climate Change 2021: The Physical Science Basis
.
Cambridge University Press
,
Cambridge
, pp.
553
672
.
Lee
J. Y.
&
Wang
B.
2014
Future change of global monsoon in the CMIP5
.
Clim. Dynam.
42
,
101
119
.
https://doi.org/10.1007/s00382-012-1564-0
Levermann
A.
,
Schewe
J.
,
Petoukhov
V.
&
Held
H.
2009
Basic mechanism for abrupt monsoon transitions
.
Proc. Natl. Acad. Sci. USA
106
,
20572
20577
.
https://doi.org/10.1073/pnas.0901414106
.
Liu
Z.
,
Mehran
A.
,
Phillips
T.
&
AghaKouchak
A.
2014
Seasonal and regional biases in CMIP5 precipitation simulations
.
Clim. Res.
60
(
1
),
35
50
.
https://doi.org/10.3354/cr01221.
Mehran
A.
,
AghaKouchak
A.
&
Phillips
T. J.
2014
Evaluation of CMIP5 continental precipitation simulations relative to satellite-based gauge-adjusted observations: CMIP5 simulations against satellite data
.
J. Geophys. Res.
119
(
4
),
1695
1707
.
https://doi.org/10.1002/2013JD021152.
Menon
A.
,
Levermann
A.
&
Schewe
J.
2013
Enhanced future variability during India's rainy season
.
GRL
40
,
3242
3247
.
doi:10.1002/grl.50583
.
Mie
R.
,
Ashfaq
M.
,
Rastogi
D.
,
Leung
L. R.
&
Dominguez
F.
2015
Dominating controls for wetter South Asian summer monsoon in the twenty-first century
.
J. Clim.
28
,
3400
3419
.
https://doi.org/10.1175/JCLI-D-14-00355.1.
Min
S. K.
,
Zhang
X.
,
Zwiers
F. W.
,
Shiogama
H.
,
Tung
Y. S.
&
Wehner
M.
2013
Multimodel detection and attribution of extreme temperature changes
.
J. Clim.
26
,
7430
7451
.
Ming
G.
,
Zhou
W.
,
Cheng
P.
,
Wang
H.
,
Xian
F.
,
Fu
Y.
,
Wu
S.
&
Du
H.
2020
Lacustrine record from the eastern Tibetan Plateau associated with Asian summer monsoon changes over the past 6 ka and its links with solar and ENSO activity
.
Clim. Dynam.
55
,
1075
1086
.
https://doi.org/10.1007/s00382-020-05312-4.
Moon
S.
&
Ha
K. J.
2020
Future changes in monsoon duration and precipitation using CMIP6
.
NPJ. Clim. Atmos. Sci.
3
,
1
7
.
doi:10.1038/s41612-020-00151-w
.
O'Neill
B. C.
,
Tebaldi
C.
,
Vuuren
D. P. V.
,
Eyring
V.
,
Friedlingstein
P.
&
Hurtt
G.
2016
The scenario model intercomparison project (ScenarioMIP) for CMIP6
.
Geosci. Model Dev.
9
,
3461
3482
.
doi:10.5194/gmd-9-3461-2016
.
Ramanathan
V.
,
Chung
C.
,
Kim
D.
,
Bettge
T.
,
Buja
L.
,
Kiehl
J. T.
,
Washington
W. M.
,
Fu
Q.
,
Sikka
D. R.
&
Wild
M.
2005
Atmospheric brown clouds: Impacts on South Asian climate and hydrological cycle
.
Proc. Natl. Acad. Sci. USA
102
,
5326
5333
.
https://doi.org/10.1073/pnas.0500656102
.
Rana
A.
,
Nikulin
G.
,
Kjellström
E.
,
Strandberg
G.
,
Kupiainen
M.
&
Hansson
U.
2020
Contrasting regional and global climate simulations over South Asia
.
Clim. Dyn.
54
,
2883
2901
.
doi:10.1007/s00382-020-05146-0
.
Revadekar
J.
&
Preethi
B.
2012
Statistical analysis of the relationship between summer monsoon precipitation extremes and foodgrain yield over India
.
Int. J. Climatol.
32
,
419
429
.
https://doi.org/10.1002/joc.2282
.
Salunke
P.
,
Keshri
n. p.
,
Mishra
S. K.
&
Dash
S. K.
2023
Future projections of seasonal temperature and precipitation for India
.
Front. Clim.
5
,
1069994
.
doi:10.3389/fclim.2023.1069994
.
Schär
C.
,
Vidale
P. L.
,
Lüthi
D.
,
Frei
C.
,
Häberli
C.
,
Liniger
M. A.
&
Appenzeller
C.
2004
The role of increasing temperature variability in European summer heatwaves
.
Nature
427
,
332
336
.
https://doi.org/10.1038/nature02300
.
Seddon
A. W. R.
,
Macias-Fauria
M.
,
Long
P. R.
,
Benz
D.
&
Willis
K. J.
2016
Sensitivity of global terrestrial ecosystems to climate variability
.
Nature
531
,
229
232
.
https://doi.org/10.1038/nature16986
.
Shah
H. L.
&
Mishra
V.
2016
Hydrologic changes in Indian subcontinental river basins (1901–2012)
.
J. Hydrometeorol.
17
,
2667
2687
.
https://doi.org/10.1175/JHM-D-15-0231.1
.
Sharmila
S.
,
Joseph
S.
,
Sahai
A. K.
,
Abhilash
S.
&
Chattopadhyay
R.
2015
Future projection of Indian summer monsoon variability under climate change scenario: An assessment from CMIP5 climate models
.
Global Planet. Change
124
,
62
78
.
doi:10.1016/j.gloplacha.2014.11.004
.
Singh
D.
,
Ghosh
S.
,
Roxy
M. K.
&
McDermid
S.
2019
Indian summer monsoon: Extreme events, historical changes, and role of anthropogenic forcings Wiley Interdisciplin
.
Rev.: Clim. Change
10
,
1
35
.
https://doi.org/10.1002/wcc.571
.
Singh
R.
,
Jaiswal
N.
&
Kishtawal
C. M.
2022
Rising surface pressure over Tibetan Plateau strengthens Indian summer monsoon rainfall over northwestern India
.
Sci. Rep.
12
,
8621
.
https://doi.org/10.1038/s41598-022-12523-8
.
Stenseth
N. C.
2002
Ecological effects of climate fluctuations
.
Science
297
,
1292
1296
.
https://doi.org/10.1126/science.1071281
.
Turner
A. G.
&
Slingo
J. M.
2009
Subseasonal extremes of precipitation and active-break cycles of the Indian summer monsoon in a climate-change scenario
.
Q. J. Roy. Meteorol. Soc.
135
,
549
567
.
https://doi.org/10.1002/qj.401
.
Varghese
S. J.
,
Surendran
S.
,
Rajendran
K.
&
Kitoh
A.
2020
Future projections of Indian Summer Monsoon under multiple RCPs using a high resolution global climate model multiforcing ensemble simulations
.
Clim. Dynam.
54
,
1315
1328
.
https://doi.org/10.1007/s00382-019-05059-7
.
Wang
Y.
,
Cheng
H.
,
Edwards
R. L.
,
He
Y.
,
Kong
X.
,
An
Z.
,
Wu
J.
,
Kelly
M. J.
,
Dykoski
C. A.
&
Li
X.
2005
The Holocene Asian monsoon: Links to solar changes and North Atlantic climate
.
Science
308
,
854
857
.
https://doi.org/10.1126/science.1106296
.
Wang
Y.
,
Cheng
H.
,
Edwards
R. L.
,
Kong
X.
,
Shao
X.
,
Chen
S.
,
Wu
J.
,
Jiang
X.
,
Wang
X.
&
An
Z.
2008
Millennial-and orbital-scale changes in the East Asian monsoon over the past 224,000 years
.
Nature
451
,
1090
1093
.
https://doi.org/10.1038/nature06692
.
Wang
Y.
,
Shen
J.
,
Wang
Y.
,
Liu
X.
,
Cao
X.
&
Herzschuh
U.
2020
Abrupt mid-Holocene decline in the Indian Summer Monsoon caused by tropical Indian Ocean cooling
.
Clim. Dynam.
55
,
1961
1977
.
https://doi.org/10.1007/s00382-020-05363-7
.
Wei
M.
&
Qiao
F.
2017
Attribution analysis for the failure of CMIP5 climate models to simulate the recent global warming hiatus
.
Sci. China Earth Sci.
60
,
397
408
.
doi:10.1007/s11430-015-5465-y
.
Xie
S. P.
,
Deser
C.
,
Vecchi
G. A.
,
Collins
M.
,
Delworth
T. L.
,
Hall
A.
,
Hawkins
E.
,
Johnson
N. C.
,
Cassou
C.
,
Giannini
A.
&
Watanabe
M.
2015
Towards predictive understanding of regional climate change
.
Nat. Clim. Change
5
,
921
930
.
https://doi.org/10.1038/nclimate2689
.
Zhang
P.
,
Cheng
H.
,
Edwards
R. L.
,
Chen
F.
,
Wang
Y.
,
Yang
X.
,
Liu
J.
,
Tan
M.
,
Wang
X.
,
Liu
J.
,
An
C.
,
Dai
Z.
,
Zhou
J.
,
Zhang
D.
,
Jia
J.
,
Jin
L.
&
Johnson
K. R.
2008
A test of climate, sun, and culture relationships from an 1810-year Chinese cave record
.
Science
322
,
940
942
.
https://doi.org/10.1126/science.1163965
.
Zhang
W.
,
Zhang
Z.
,
Liao
Z.
,
Wang
Y.
,
Chen
S.
,
Shao
Q.
&
Wang
Y.
2019
Changes in the Asian monsoon climate during the late last interglacial recorded in oxygen isotopes of a stalagmite from the Yongxing Cave, central China
.
J. Asian Earth Sci.
179
,
211
218
.
https://doi.org/10.1016/j.jseaes.2019.04.024
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).