The extreme climate event change trend forecast in the Huaihe River Basin, China

Currently, as global temperatures continue to rise, we witness an increase in the frequency, intensity, and duration of extreme weather events. This trend has significant consequences for natural ecosystems, human societies, and economies worldwide. Extreme climate events occur frequently (Toreti et al. 2019; Yin & Sun 2019). Previous studies have shown that the frequency of extreme high-temperature events has increased, while the frequency of extreme cold events has decreased in recent years (Alexander et al. 2006). Some scholars have studied the spatial and temporal change trends of extreme climates in different regions, such as America (Aguilar et al. 2005), Africa (New et al. 2006; Aguilar et al. 2009; Adu-Prah et al. 2019), the Middle East (Zhang et al. 2005; Todaro et al. 2022), Asia (Klein Tamg et al. 2006; Hefzul Bari et al. 2016; Abu Hammad et al. 2022; Wijeratne et al. 2023), Europe (Mersin et al. 2022), China (Ren et al. 2010; You et al. 2011; Jiang et al. 2022; Liu et al. 2023), the Qinghai‒Tibet Plateau (You et al. 2008), the Beijing–Tianjin–Hebei region (Zhao et al. 2019), the coastal area and the Yangtze River Basin (Cui et al. 2019), to discover that extreme temperature events in different regions have a similar development trends, which shows an increasing trend for extreme high-temperature events and a decreasing trend for extreme low-temperature events. The trend of extreme low temperature is greater than the trend of extreme high temperature. However, extreme precipitation events vary significantly among different regions. For example, the changes in extreme precipitation in Southwest China, Eastern China and the northwestern, middle and lower reaches of the Yangtze River show an upward trend, while the changes in extreme precipitation in Central, North and Northeast China show a downward trend (Zhang & Wan 2005; Gao & Xie 2016). Extreme climate change has a heavy impact on agriculture, the national economy, social development and the ecological environment (Fu et al. 2013). Therefore, it is highly important to study and evaluate the spatial and temporal evolution patterns of extreme climate change to formulate strategic measures to adapt to climate change.

In recent years, the researchers both at home and abroad have made important findings in the field of extreme climate change. Wei et al. (2022) studied simulation and projection of climate extremes in China by multiple Coupled Model Intercomparison Project Phase 6 (CMIP6) models. This study evaluates the ability of 23 climate models from CMIP6 in simulating extreme climate events over China. Mitra et al. (2011) carried out a study on experimental real-time multi-model ensemble (MME) forecast of rainfall during monsoon 2008. Global models and data assimilation techniques are being improved for monsoon/tropics. However, MME forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and handling model uncertainties. As major centers are exchanging model output in near real-time, MME is a viable and cost-effective approach to enhance forecasting skill and information content (Ahmed et al. 2020; Jose et al. 2022).

The Huaihe River basin, located in the north‒south climate transition zone of China, is an important geographical and ecological boundary line between the north and the south of China and belongs to a fragile ecological environment region in China (Huaijun et al. 2017; Zhang & Zhou 2020). Under the situation of global warming, the temperature in the Huaihe River basin is increasing. The spatial and temporal distributions of precipitation are uneven. Extreme high-temperature events increase and extreme low-temperature events decrease. The change trend of annual extreme precipitation shows obvious regional differences (Wu et al. 2019). Due to special geographical environmental conditions, the instability of climate systems and water circulation systems has increased, which has resulted in an increase in the frequency and intensity of extreme climate events (Cao et al. 2020). The natural disasters caused by extreme climate events have created severe challenges for the water supply, ecological environment and food security in the basin.

Therefore, the objectives of this paper are as follows: (1) based on the systematic analysis of climate change trend, the extreme climate indices were selected; (2) the MME averaging method was proposed to carry out climate simulation and forecast both for measured and simulated data series in the Huahe River Basin; (3) extreme climate value distribution evaluation methods for different recurrence periods were studied which include generalized extreme value (GEV) distribution and reversible jump Markov chain Monte Carlo (RJMCMC) methods to obtain extreme precipitation for different recurrence periods. The studied method can be used to detect time series mutation points; (4) based on three climate models (i.e., GFDL, FGOALS and CCSM4) (Masahi et al. 2010; Watanabe et al. 2011; Timm et al. 2015), the change trends of extreme climatic events under different scenarios (i.e., RCP 2.6, RCP 4.5, and RCP 8.5) were studied by using the MME average method; (5) taking the Huaihe River Basin as a case study, temperature change trend and rainfall change trend in the Huaihe River Basin under the RCP 2.6, RCP 4.5, and RCP 8.5 scenarios from 2010 to 2100 were obtained.

The results of this study are highly important for understanding the frequency, intensity and development trends of extreme climate changes in the future, as well as for adopting corresponding strategies to promote high quality social and economic development in the Huaihe River Basin.

The methods used in this paper are as follows:

• (1) Based on the systematic analysis on climate change trend, the extreme climate indices were selected.

• (2) The MME averaging method was proposed to carry out climate simulation and forecast both for measured and simulated data series in the Huahe River Basin.

• (3) Extreme climate value distribution evaluation methods for different recurrence periods were studied which include GEV distribution and reversible jump Markov chain Monte Carlo (RJMCMC) methods to obtain extreme precipitation for different recurrence periods. The studied method can be used to detect time series mutation points.

• (4) Based on three climate models (GFDL, FGOALS, and CCSM4), the change trends of extreme climatic events under different scenarios (RCP 2.6, RCP 4.5, and RCP 8.5) were studied by using the MME average method.

When selecting Coupled Model Intercomparison Project (CMIP) models for climate research or projections, researchers often face the decision between using CMIP5 and CMIP6 global climate models (GCMs). Both have their advantages and disadvantages, which can significantly impact the outcomes of climate studies. The choice between CMIP5 and CMIP6 GCMs depends on the specific needs and constraints of the research project. If we prioritize established reliability and extensive historical data, CMIP5 models might be more suitable. On the other hand, if cutting-edge science, improved resolution, and comprehensive earth system integration are critical, then CMIP6 models would likely be the better option despite the associated challenges.

CMIP5 is a significant effort in climate modeling that involves multiple GCMs from various research institutions around the world.

In this paper, the choice of CMIP5 GCMs is driven by their scientific rigor, comprehensive nature, ensemble approach, advanced technology, historical validation, scenario diversity, open access, and the continuity they offer in advancing climate modeling research. These factors collectively contribute to making CMIP5 a valuable resource for studying climate change and its implications.

In this paper, the selected extreme climate indices include annual maximum 1-day precipitation (RX1DAY), annual maximum 5-day precipitation (RX5DAY), annual total days with rainfall greater than 50 mm (i.e., daily rainfall volume ≥ 50 mm) (R50mmday), annual consecutive wet days (CWD), annual total daily rainfall greater than 99% of the location points (daily rainfall ≥ 99% of the location points) (R99), daily maximum temperature (TXx) and daily minimum temperature (TNn) and etc., see Table 1 (Ren et al. 2023).

In this paper, the distributions of the measured precipitation data and the CMIP5 climate model are analyzed via GEV distribution and reversible jump Markov chain Monte Carlo (RJMCMC) methods, respectively, to obtain extreme precipitation for different recurrence periods (Wei et al. 2022; Kara et al. 2024).

The reversible jump Markov chain Monte Carlo (RJMCMC) method is a mathematical model based on the sequence of extreme events, in which the time series of extreme events can be considered as a reversible jump Markov chain. First, a reversible jump Markov chain is used to identify mutation years in the sequence. Then, Bayesian theory (Zhao & Chu 2010) is used to obtain the probabilities corresponding to the mutation points in the selected sequence and to identify the mutation point positions. The studied method can be used to detect time series mutation points, which are composed of Poisson distribution random data series under different parameters.

The computational steps of the Reversible Jump Markov Chain Monte Carlo method (RJMCMC) algorithm are as follows:

• 1) Determine the jump probability matrix between different model assumptions;

• 2) Obtain u_k by sampling from the simulated distribution ⁠;

• 3) Set the transformation matrix ⁠;

• 4) Calculate the transfer ratio r from H_k to ⁠;

The Huaihe River Basin is situated between the Yangtze River and the Yellow River in the eastern region of China, and the geographical location is between 30°55′–36°36′N and 111°55′–121°25′E (see Figure 1), with a basin area of 270,000 km². The Huaihe River Basin covers 181 counties (cities) in the Hubei, Henan, Anhui, Shandong and Jiangsu provinces. The total population in the river basin is 165 million, and the average population density is 611 people/km², which ranks first in terms of the population density of all major river basins in China. The Huaihe River Basin is located in the climate transition zone between northern and southern China (Zhen et al. 2022). The average annual temperature spans from 11 to 16 °C. The highest monthly mean temperature is approximately 25 °C, which occurs in July. The lowest monthly average temperature is approximately 0 °C, which occurs in January. The highest extreme temperature is 44.5 °C, and the lowest is −24.1 °C. Therefore, the Huaihe River Basin is an area with extreme temperature events in China. The annual average precipitation in the Huaihe River Basin is approximately 600–920 mm, and the distribution trend of precipitation in the basin decreases from south to north, with less precipitation in plains and more precipitation in mountain areas and less precipitation in inland areas than that in coastal areas.

Overall, the spatial distributions of the daily maximum and minimum temperatures in the Huaihe River basin under the three RCP scenarios are consistent, showing an upward trend from the upper reaches to the middle and lower reaches of the basin.

The increasing trend of annual precipitation under the RCP8.5 scenario is obvious. Compared with the RCP 2.6 scenario, there are more years with low annual precipitation under the RCP 4.5 and RCP 8.5 scenarios before 2040, which shows that the annual precipitation change in the Huaihe River Basin will have a weak increasing trend in the future. By the late 21st century, the increasing trend of annual precipitation in the basin will be slightly greater due to high greenhouse gas concentration.

As shown in Figure 5, the maximum 1-day precipitation under the RCP 2.6 scenario varies between 110 and 150 mm, during which the peak periods mainly occur in 2023, 2040, 2041, 2070 and 2090. The maximum 1-day precipitation under the RCP 4.5 scenario varies between 105 and 160 mm, its trough value occurs in 2030, and its peak value occurs in 2088. The maximum 1-day precipitation under the RCP 8.5 scenario varies from 115 to 165 mm. Compared with those under the RCP 2.6 and RCP 4.5 scenarios, the maximum 1-day precipitation under the RCP 8.5 scenario shows an obvious increasing trend, which indicates that the maximum 1-day precipitation in the basin under the RCP 8.5 scenario has a high probability of extreme rainstorm events. The maximum 5-day precipitation fluctuates in the range from 180 to 380 mm for the three scenarios, in which the peak value (360 mm) of the maximum 5-day precipitation under the RCP 2.6 scenario occurs in 2040, the peak value (320 mm) of the maximum 5-day precipitation under the RCP 4.5 scenario appears in 2067, and the peak value (380 mm) of the maximum 5-day precipitation under the RCP 8.5 scenario appears in 2068. In general, the lowest fluctuation range of the maximum 5-day precipitation under the RCP 2.6 scenario can be found, and the maximum 5-day precipitation shows a strong fluctuation under the RCP 8.5 scenario.

Comparing the spatial variation in extreme rainfall from 2020–2054 and 2055–2100 with that in the baseline period (the measured data series from 1961–2005) under the three scenarios and taking 1-day rainfall as an example, the change trend of rainfall in the 2020–2054 and 2055–2100 periods under the RCP 2.6 scenario will be intensified. Extreme rainfall events occur mainly in the middle and lower reaches of the Huaihe River. The change amplitudes of the maximum 1-day rainfall (compared with the 1-day rainfall in the baseline period, i.e., measured data series from 1961 to 2005) from 2020 to 2100 under the RCP 2.6 and RCP 4.5 scenarios are 30–120 and 50–150 mm, respectively. The change amplitude of the maximum 1-day rainfall in the middle reaches is greater than that in the downstream reaches. The greatest change in the maximum 1-day rainfall occurs under the RCP 8.5 scenario, i.e., 50–140 mm in the middle-21st century and 120–160 mm in the late 21st century. The increasing trend of the maximum 1-day rainfall in the middle reaches is significant.

A comparison of the forecasted maximum rainfall for different recurrences and duration under different scenarios with the historical measured long-term data series (1961–2005) in the Huahe River Basin is shown in Table 2.

As shown in Table 2, the maximum 1-day and the maximum 5-day precipitation for different recurrences (10 year⁻¹, 2010 year⁻¹, 50 year⁻¹, 100 year⁻¹) under three scenarios (RCP 2.6, RCP 4.5, RCP 8.5) in the Huaihe River Basin are all greater than those in the historical period (measured data series from 1961 to 2005). Moreover, the maximum 1-day and maximum 5-day precipitation amounts under the RCP 8.5 scenario in the same recurrence period are greater than those under the RCP 4.5 and RCP 2.6 scenarios.

Climate change trend would result in changes in the frequency, intensity, spatial range and duration of extreme climate events, and would also cause unprecedented extreme climate events. China is one of the countries which is severely affected by natural disasters, and also one of the countries with high frequency and intensity of extreme climate events. The Huaihe River Basin is located in the climate transition zone between northern and southern China. As global climate change increases and the rapid social and economic development, the pressure on water resources, ecology and environment, the prevention and response to extreme climate events have become more and more severe and complex.

On spatial and temporal variation of extreme climate change trend events, at present, the studies are mainly concentrated in the following aspects; (1) Characterization of extreme climatic events. Based on the definition of single station monitoring technology and data series, the ‘climate extreme value (threshold)’ is used to quantify abnormal weather and climate phenomena. Extreme events can be considered to occur when the climate elements reach the defined threshold. Obviously, the determination of the threshold is greatly influenced by human factors. (2) Spatial and temporal variation trend characteristics of extreme climate events. The trend test, mutation analysis, wavelet transformation and geo-statistic techniques are mainly used to analyze the spatial and temporal distribution rules of extreme climatic events in a certain river basin or a region. (3) Extreme climatic trend events are analyzed based on the characteristics of the spatial and temporal distribution of the stationary extreme value function. An extreme climatic event is a random variable, which could be fitted and extracted for recurrence time levels by using the GEV distribution or the generalized Pareto distribution (GPD). In the traditional extreme value function analysis, the simulation of extreme climatic trend events is often based on the stationary marginal distribution function, which makes it easy to ignore the influence of the non-stationary characteristics of hydrological variables in the climate change trend scenarios. The Mann–Kendall test and Innovative Climate Change Trend Analysis are statistical tools which can also be used to detect trends in time series data, particularly in the context of climate change.

In this paper, the MME average method was used to carry out analysis on measured data series (1961–2005). The distributions of the measured precipitation data and the CMIP5 climate model are analyzed via GEV distribution and reversible jump Markov chain Monte Carlo methods, respectively, to obtain extreme precipitation for different recurrence periods. The reversible jump Markov chain Monte Carlo (RJMCMC) method is a mathematical model based on the extreme events sequence, in which the time series of extreme events can be considered as a reversible jump Markov chain.

In this paper, Bayesian theory is used to obtain the probabilities corresponding to the mutation points in the selected sequence and to identify the mutation point positions. The studied method can be used to detect time series mutation points, which are composed of Poisson distribution random data series under different parameters. It can be seen from comparison of the studied results with the measured data series (1961–2005) that the general distribution is consistent.

Based on three climate models (i.e., GFDL, FGOALS, and CCSM4), the spatial and temporal variation characteristics of the extreme climate in the Huaihe River Basin under three scenarios (RCP 2.6, RCP 4.5, and RCP 8.5) were forecasted. The results showed that the rates of increase in the daily maximum temperature under the three scenarios (RCP 2.6, RCP 4.5, and RCP 8.5) were 0.052 °C 10 year⁻¹, 0.170 °C 10 year⁻¹and 0.470 °C 10 year⁻¹, respectively. The rates of increase in the daily minimum temperature were 0.029 °C 10 year⁻¹, 0.170 °C 10 year⁻¹, and 0.460 °C 10 year⁻¹, respectively. The spatial distribution of the daily maximum and minimum temperatures shows a consistent trend, increasing from the upper reaches to the middle and lower reaches of the Huaihe River. Extreme rainfall analysis in different recurrence periods (10 year⁻¹, 20 year⁻¹, 50 year⁻¹, and 100 year⁻¹) under the three scenarios revealed that the maximum 1-day and 5-day rainfall amounts were greater than those in the historical periods (from 1961 to 2005) in the Huaihe River Basin. Moreover, the maximum 1-day and 5-day rainfall amounts under the RCP 8.5 scenario were greater than those under the RCP 4.5 and RCP 2.6 scenarios at the same recurrence periods. The annual precipitation in the Huaihe River Basin shows a weak increasing trend. It can be shown from the study results that by the late 21st century, climate change (both temperature and rainfall) under the future climate change impact scenarios will increase greatly.

According to study results, some indices were discovered with obvious change trend in the future. Significant change indices at the different sites could reach from 14 to 21%. The regional change trends of Rx1day and Rx5day will be slightly significant either in space or time.

The Rx1day and Rx5day in different recurrences are increasing from the northwest to the southeast within the basin, which may be related to the gradual strengthening trend of the southeast monsoon. Due to the regulation effect of the ocean on the temperature in the eastern part of the basin, the closer to the ocean, the more influence would be by the ocean, which results in the gradual increase of TXx from east to west. Since TNn is treated with a non-stationary model in this study, the recurrence would vary in the future.

It can be seen from the estimation of climate change under the RCP 2.6, RCP 4.5, and RCP 8.5 future scenarios that the daily maximum temperature increase rates under the three scenarios are 0.052 °C 10 year⁻¹, 0.170 °C 10 year⁻¹, and 0.470 °C 10 year⁻¹, respectively, and the daily minimum temperature increase rates are 0.029 °C 10 year⁻¹, 0.170 °C 10 year⁻¹, and 0.460 °C 10 year⁻¹, respectively. The spatial distributions of the daily maximum and minimum temperatures are consistent and show a rising trend from the upper reaches to the middle and downstream reaches of the Huaihe River Basin.

According to the precipitation change trend analysis, the maximum 1-day precipitation varies between 105 and 165 mm under the three scenarios. Compared with those under the RCP 2.6 and RCP 4.5 scenarios, the change amplitude of the maximum 1-day precipitation under the RCP 8.5 scenario is the greatest, and the probability of extreme precipitation events under the RCP 8.5 scenario is also the highest among the three scenarios. The maximum 5-day precipitation fluctuates in the range of 180–380 mm for the three scenarios. Overall, the fluctuation range of the maximum 5-day precipitation under the RCP 2.6 scenario is the lowest, and the fluctuation range of the maximum 5-day precipitation under the RCP 8.5 scenario is the highest among the three scenarios.

It can also be seen from the comparative analysis of the spatial variation in extreme precipitation from 2020 to 2054 and 2055 to 2100 with the measured long-term data series from 1961 to 2005 that the maximum 1-day precipitation during 2020–2054 and 2055–2100 under the RCP 2.6 scenario shows a rising trend. The extreme rainfall events occur mainly in the middle and lower reaches of the Huaihe River. The change amplitudes (predicted value vs. measured data series from 1961 to 2005) of the maximum 1-day precipitation from 2020 to 2100 under the RCP 2.6 and RCP 4.5 scenarios are 30–120 and 50–150 mm, respectively.

Compared with the measured long-term data series (1961–2005), the change amplitude (forecasted value compared with the measured data series) of the maximum 1-day precipitation in the middle reaches is greater than that in the upper and lower reaches. The change amplitude of the maximum 1-day rainfall under the RCP 8.5 scenario is the greatest among the three scenarios, i.e., 50–140 mm in the middle-21st century and 120–160 mm in the late 21st century. The increasing trend of the maximum 1-day rainfall in the middle reaches is significant.

The analysis of extreme precipitation for different recurrence periods revealed that the maximum 1-day and maximum 5-day precipitation amounts under the three future scenarios for the recurrence periods of 10 year⁻¹, 20 year⁻¹, 50 year⁻¹, and 100 year⁻¹ are all greater than those in the historical periods. Moreover, the maximum 1-day and 5-day precipitation amounts under the RCP 8.5 scenario are greater than those under the RCP 4.5 and RCP 2.6 scenarios for the same recurrence period.

Under the future scenarios, extreme rainfall events will occur mainly in the middle and lower reaches of the Huaihe River. Therefore, it is necessary to pay close attention to climate change in the Huaihe River Basin to formulate comprehensive water resources management plans, to develop effective prevention schemes, to reduce the adverse effects on agricultural production and people's lives, and to ensure regional economic higher quality development.

The extreme climate event change trend forecast is a critical area of research as the frequency and intensity of these events are expected to increase due to climate change. Future research directions in this field could include the following: as improvement of climate models, data integration and analysis, regional climate projections, understanding extreme event drivers, impact assessments and vulnerability studies, scenario analysis, adaptation strategies, climate engineering as well as public awareness and policy development. These research directions aim to improve our understanding of extreme climate events and enhance our ability to predict, mitigate, and adapt to their impacts.

The author declares that there are no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

This article does not contain any studies performed by other authors.

The author consents to participate in the work under ethical approval and compliance with ethical standards.

All the data in the paper can be published without any competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

The study conception and design, material preparation, data collection and analysis were performed by K.Z.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.