The effect of climate change on meteorological drought and hydrological drought assessment in Kohgilouye and Boyer Ahmad Province of Iran

Natural risks are constantly present in human existence, with weather mechanisms playing a significant role in many of these issues. Among natural disasters, drought ranks as one of the most dangerous events due to its slow onset and prolonged impact on various aspects of human life. It is often difficult to predict the exact onset and termination of droughts since their effects develop gradually over large geographic areas. Drought can also exacerbate or trigger other extreme weather events such as strong winds, storms, and floods, intensifying its overall impact (Bryant 1991; Keyantash & Dracup 2002; Jones & Moberg 2003). The significance of climate as a key driver of ecosystem dynamics further underscores the necessity of understanding drought occurrences. Even minor climatic variations can have profound effects on environmental systems. Climate change, primarily driven by human activities and industrialization, has led to increased greenhouse gas concentrations, contributing to rising global temperatures and intensifying natural hazards such as storms, floods, droughts, and dust storms. Researchers recognize climate models as the most reliable tools for assessing climate change phenomena (IPCC 2023).

Researchers have ranked drought as the most dangerous natural hazard phenomenon because of its extreme impact. Researchers, such as Arias et al. (2021), Dai (2011) and Vicente-Serrano et al. (2020), have ranked drought as one of the most dangerous natural hazards due to its extensive and long-lasting impacts. One of the most significant natural calamities, droughts have a gradual and sluggish impact on many facets of human existence. As a climatic disaster, this occurrence has a significant negative impact on populations by limiting their access to water supplies and causing other negative social, economic, and environmental effects (Amiri et al. 2024).

Iran, located between latitudes 20° and 40° north, falls within the geographical drought and desert belt, making it highly vulnerable to drought conditions. The country's average annual rainfall is approximately 250 mm, significantly lower than the global average. Due to the uneven distribution of rainfall, large portions of Iran are classified as arid or semi-arid regions. Kohgiluyeh and Boyer Ahmad Province, a vital agricultural and water resource hub, face similar climatic challenges. Understanding the impacts of climate change on the region's water resources is essential for effective water management and disaster preparedness. In recent years, drought indices have been widely used for monitoring and assessing drought conditions. Several studies have examined different drought indicators, such as standardized precipitation evapotranspiration (SPEI), reconnaissance drought index (RDI), effective reconnaissance drought index (eRDI), Z score index (ZSI), standardized streamflow index (SSI), and SDI, to analyse drought characteristics at national and international levels (Edossa et al. 2010; Spinoni et al. 2015; Li et al. 2016; Gao et al. 2018; Wahla et al. 2022; Zeynali & Faridpour 2022; Amiri et al. 2024; Baloei et al. 2024). However, despite extensive research on drought indicators, a comprehensive analysis of the effects of climate change on drought in Kohgiluyeh and Boyer Ahmad Province remains lacking.

Given the significance of Kohgiluyeh and Boyer Ahmad Province for Iranian agriculture and water resources, an in-depth investigation into the region's drought conditions is necessary. Although several national and international studies have examined drought trends, the province lacks a detailed study that integrates multiple drought indicators to provide a comprehensive understanding of drought patterns. Additionally, the long-term impact of climate change on drought conditions in this region has not been sufficiently explored. This study aims to address these gaps by assessing the drought situation in Kohgiluyeh and Boyer Ahmad Province over the period 2024–2047 using multiple drought indicators.

To achieve this objective, this study employs the HADGEM3-GC31-LL climate model under two climate scenarios, SSP1.2-6 and SSP5.8-5, to project future precipitation and temperature trends. The SSP1.2-6 scenario represents a future where society adopts sustainable practices, leading to a significant reduction in greenhouse gas emissions. This scenario assumes that global efforts focus on environmental conservation, clean energy expansion, and controlled population growth, resulting in lower climate change impacts. On the other hand, SSP5.8-5 describes a future characterized by rapid economic growth and continued reliance on fossil fuels. In this scenario, industrialization and high energy consumption contribute to increased greenhouse gas emissions, leading to more severe climate changes, including rising temperatures and extreme weather events. By analyzing these contrasting scenarios, this study aims to provide a detailed and multidimensional assessment of future drought conditions in the region. The results of this research will contribute to improved water resource management, climate change adaptation strategies, and policy-making efforts aimed at mitigating the adverse effects of drought in Kohgiluyeh and Boyer Ahmad Province.

In this research, the rainfall and temperature data of Yasuj and Dogonbadan stations, which have the most complete data from 2000 to 2023, have been used. DrinC software was used to calculate RDI, eRDI, and standardized precipitation index (SPI) drought indices. For the groundwater resource index (GRI) and SSI index, the groundwater level data of three piezometric wells and two hydrometric station data in the region from 2000 to 2023 have been used. The features and specifications of the studied stations, three piezometric wells, and two hydrometric stations are shown in Table 1.

Drought Indices Calculator (DrinC) is a specialized software developed for calculating various drought indices, including the RDI, eRDI, and SPI. The software was designed to facilitate drought assessment and monitoring by incorporating precipitation and temperature data to evaluate meteorological drought severity over different time scales (Tigkas et al. 2015)

In this study, DrinC software was used to compute RDI, eRDI, and SPI indices based on historical rainfall and temperature records from the Yasuj and Dogonbadan stations for the period 2000–2023. The software applies probabilistic and statistical methods to standardize precipitation and evapotranspiration data, ensuring accurate drought characterization. The SPI index was used to assess precipitation variability, while RDI and eRDI indices incorporated potential evapotranspiration (PET) to provide a more comprehensive evaluation of drought severity, particularly under climate change conditions.

DrinC's contribution to this research lies in its ability to process large climate datasets efficiently and generate reliable drought indices, which were later analyzed to identify drought trends and their potential implications for water resource management. The results derived from DrinC were used in subsequent discussions to compare different drought conditions and validate climate model projections.

In general, to calculate the eRDI and RDI indices, in addition to the precipitation data, evaporation and transpiration data are needed, which were calculated using the average temperature data and the Torrent-White method.

Also, in the eRDI method, in addition to the value of potential evaporation and transpiration, we need effective precipitation in the region. The difference between the RDI method and the eRDI method is that in the eRDI method, we use the effective rainfall of the region. In this research, we have used the United State Bureau of Reclamation (USBR) method to calculate effective precipitation.

The SPI is a widely used metric to describe the level of dryness in the weather. It illustrates how much less rain there is across various time periods using rainfall data. The SPI is a valuable tool for studying and mapping droughts since it can be used to measure how severe, long-lasting, frequent, and broad a drought is. In order to predict droughts, the SPI has also been coupled with models such as the Markov chain model. As a result, the SPI is crucial for understanding and preparing for dry weather in many global locations (Bagheri 2016; Sobral et al. 2019). The SPI, which is based on a probabilistic approach to rainfall, is used to track both dry and rainy seasons. A drought event occurs when the index consistently takes values that are less than or equal to unity. This process keeps happening until the index starts to take on positive values. An episode's duration is determined by the amount of time that elapses between its beginning and end. The amount of a drought episode may be calculated by summing the indicator's readings for each month of the drought. When the index takes values greater than 2.00, it is considered an extraordinarily rainy period; when the index takes values less than −2.00, it is considered an intense drought. Values between 0.99 and −0.99 indicate about normal conditions (Sobral et al. 2019).

The SPI is calculated by fitting a probability density function to the rainfall frequency distribution and calculating the sum outside the time frame of interest. This approach is applied once to each site in the interval, as well as to each month or time scale from the rainfall data time series. Every probability density function inside the traditional normal distribution is then converted. Thom (1958) reported that the distribution of climate precipitation data follows a gamma distribution, one of the most prevalent distributions in technical hydrology. For this positive asymmetric distribution, the variable is specified only for positive values (Thom 1958). The equations related to this index are presented in Table 2.

Table 2

The equations related to the SPI (Tsesmelis et al. 2023)

Equations .	Parameters .
(1)	α > 0 = the form factor
(2)	β > 0 = the scale factor
(3)	x > 0 = the amount of rainfall
(4)	Γ(α) = gamma function
(5)	n = the number of different precipitation series
(6)	G(x) = the cumulative probability
(7)	x = cumulative probability because the gamma distribution is uncertain for x = 0 and that a rainfall distribution can contain zero
For 0 < H(x) < 0.5 (8)	C0 = 2.515517 C1 = 0.802853 C2 = 0.010308 d1 = 1.432788 d2 = 0.189269 d3 = 0.001308
(9)
For 0.5 < H(x) < 0.1 (10)
(11)

Equations .	Parameters .
(1)	α > 0 = the form factor
(2)	β > 0 = the scale factor
(3)	x > 0 = the amount of rainfall
(4)	Γ(α) = gamma function
(5)	n = the number of different precipitation series
(6)	G(x) = the cumulative probability
(7)	x = cumulative probability because the gamma distribution is uncertain for x = 0 and that a rainfall distribution can contain zero
For 0 < H(x) < 0.5 (8)	C0 = 2.515517 C1 = 0.802853 C2 = 0.010308 d1 = 1.432788 d2 = 0.189269 d3 = 0.001308
(9)
For 0.5 < H(x) < 0.1 (10)
(11)

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Drought indexes that gauge how bad a drought is are the eRDI and the RDI. The eRDI focuses on droughts in agriculture and the amount of water that plants can efficiently consume (Tigkas et al. 2022). Tigkas et al. (2017) using the eRDI helps the DrinC programme analyse and characterize agricultural droughts more rapidly and precisely (Tigkas et al. 2022). On the other hand, the RDI is a more all-encompassing drought index that can assess any kind of drought. While both indices may be used to monitor and assess drought conditions, the eRDI is particularly helpful in cases of agricultural drought (Zarei et al. 2019). The RDI was established as a wide meteorological measure for the purpose of assessing drought. The standardized RDI (RDIst), the normalized RDI (RDIn), and the starting value (αk) are the three ways to express the RDI. A monthly time scale is used to display the initial value (αk), which can be calculated monthly, seasonally, or annually. The eRDI, like the RDI index, replaces total precipitation with effective precipitation (Pe). A severe drought is defined by an index value of less than −2.00 in the RDI and eRDI, whereas a wet period is defined as one in which the index value is more than 2.00. Table 3 presents the equations related to the RDI (Zarei et al. 2019).

Table 3

The equations related to the RDI (Zarei et al. 2019)

Equation .	Parameters .
(12)	Pij = the precipitation PETij = the potential evapotranspiration
(13)	= the arithmetic means of αk values
(14)	= the arithmetic means of yk = the standard deviation

Equation .	Parameters .
(12)	Pij = the precipitation PETij = the potential evapotranspiration
(13)	= the arithmetic means of αk values
(14)	= the arithmetic means of yk = the standard deviation

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Similar to the RDI, the eRDI substitutes effective precipitation (Pe) for total precipitation. Its relationships are presented in Table 4.

Table 4

The equations related to eRDI (Zarei et al. 2019)

Equation .	Parameters .
(15)	Pej = effective precipitation PETij = the potential evapotranspiration
(16)	= the arithmetic means of αk values
(17)	= the arithmetic means of yk = the standard deviation

Equation .	Parameters .
(15)	Pej = effective precipitation PETij = the potential evapotranspiration
(16)	= the arithmetic means of αk values
(17)	= the arithmetic means of yk = the standard deviation

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The GRI is an index that measures and predicts drought in areas with a Mediterranean climate (Mendicino et al. 2008). It is a new index that analyses how drought affects groundwater resources over space and time, using data on groundwater levels (Mohebbi Tafreshi et al. 2017). The GRI has been applied to evaluate the condition of hydrogeological drought in different places. However, the GRI is not widely used for describing meteorological drought, which is usually done using the SPI (de Oliveira-Júnior et al. 2018). Mendicino et al. (2008) presented the GRI as a trustworthy metric for modelling, tracking, and predicting the Mediterranean region's drought condition. In this index, like SPI, a wet period is defined as one in which the index takes values larger than 2.00, and an extreme drought is defined as one in which the index takes values less than −2.00 (Mohebbi Tafreshi et al. 2017). The Equation (18) is used to compute the value of the GRI:

The SSI is a hydrological metric that evaluates stream discharge in relation to long-term historical records. It measures the deviation of the current stream flow from the median flow for a specific time period (Zalokar et al. 2021). The SSI aims to transform monthly flow data into a standard normal distribution with a mean of 0 and a variance of 1. Similar to the SPI, SSI values exceeding 2.00 indicate wet periods, while values below −2.00 signal extreme drought conditions (Salimi et al. 2021). The SSI formula is similar to the SPI index, which is given in Table 2, except that surface water discharge is used instead of precipitation (Vicente-Serrano et al. 2012).

The Sixth Assessment Report (AR6) by the Intergovernmental Panel on Climate Change (IPCC) builds upon its predecessor, the Fifth Assessment Report (AR5), by refining climate projections with updated models and socioeconomic pathways (SSPs). AR5 introduced representative concentration pathways (RCPs) to project future climate scenarios based on greenhouse gas emissions. AR6 advances this approach by incorporating shared SSPs, which integrate socioeconomic factors such as population growth, technological development, and global sustainability efforts (Riahi et al. 2017).

The primary distinction between AR6 and AR5 is the transition from RCPs to SSPs, which allows for a more comprehensive analysis of climate change by considering both emissions and societal responses. AR6 also benefits from improved climate models with higher resolution, better representation of physical processes, and updated observational datasets, enhancing the accuracy of projections (IPCC 2021).

For this study, two SSP-based scenarios – SSP1.2-6 and SSP5.8-5 – are utilized. The SSP1.2-6 scenario represents a sustainable pathway where global cooperation leads to reduced emissions and environmental preservation, limiting global warming. Conversely, SSP5.8-5 envisions a fossil-fuel-intensive world with high emissions, leading to more severe climate impacts. By using these scenarios, AR6 provides a clearer picture of potential future climate conditions compared to previous reports, making it a valuable tool for assessing drought trends and water resource management (Riahi et al. 2017).

To ensure the reliability of the HADGEM3-GC31-LL model projections, a validation process was conducted by comparing historical simulated climate data with observed meteorological records from the Yasuj and Dogonbadan stations (2000–2023). The observed data, obtained from local meteorological agencies, included monthly precipitation and temperature records. The validation was performed by employing statistical performance metrics, including mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and Pearson's correlation coefficient (R²) (Kuhlbrodt et al. 2018).

The Figure 3 illustrates temperature and precipitation changes in the Dogonbadan station under different climate change scenarios (SSP1.2-6 and SSP5.8-5) compared to basic conditions. In Figure 3(a), temperature trends exhibit a similar seasonal pattern across all scenarios, with a steady increase from January, peaking in June, and then declining towards December. The differences between the scenarios appear minimal, with SSP5.8-5 showing slightly higher temperatures than SSP1.2-6 and basic conditions. In Figure 3(b), precipitation variations indicate significant seasonal fluctuations, with peaks in February and April, followed by a sharp decline from May to September. The SSP5.8-5 scenario generally exhibits higher precipitation levels in peak months compared to the basic condition and SSP1.2-6, which shows lower precipitation during most months. The trends suggest that while temperature changes are relatively uniform across scenarios, precipitation patterns display greater variability, potentially indicating stronger climatic shifts under different climate change scenarios.

In the Yasouj station, the temperature fluctuations in scenario 585 have been very impressive and high. In this station, the amount of precipitation increases in the months of April, February, November, and September, and decreases significantly in the month of July. The graph above illustrates the projected temperature and precipitation changes under two climate scenarios (SSP1-2.6 and SSP5-8.5) at the Yasouj station. In Figure 4(a), temperature trends show that SSP5-8.5, the high-emission scenario, leads to consistently higher temperatures throughout the year compared to SSP1-2.6, which represents a more sustainable climate trajectory. The temperature peak occurs during the summer months, with SSP5-8.5 exhibiting a more pronounced rise. In Figure 4(b), precipitation patterns fluctuate more significantly, with both scenarios showing seasonal variations. The SSP5-8.5 scenario tends to produce slightly more precipitation during peak months (e.g., March, April, and September), whereas SSP1-2.6 maintains a relatively moderate pattern. However, the overall trend indicates higher variability in precipitation under SSP5-8.5, suggesting that extreme weather events could become more frequent under this scenario. These findings highlight the potential for increased warming and altered precipitation patterns under high-emission pathways, reinforcing the importance of mitigation strategies to stabilize climatic conditions.

After examining temperature changes in basic conditions and climate change, drought indices, SPI, RDI, and eRDI have also been calculated for basic conditions and climate change based on scenarios SSP1.2-6 and SSP 5.8-5.

The provided graphs above illustrate the variations in different drought indices (SPI, RDI, and eRDI) across three climate conditions: SSP1-2.6, SSP5-8.5, and the basic condition. The SPI fluctuates significantly in all scenarios, reflecting variations in precipitation levels. However, the SSP5-8.5 scenario exhibits more extreme fluctuations compared to SSP1-2.6, suggesting increased climate variability and a higher likelihood of both droughts and wet periods. Similarly, the RDI trends indicate that SSP5-8.5 leads to more pronounced variations and frequent extreme drought events, underscoring the impact of warming on drought intensity and persistence. The eRDI follows a similar pattern, with SSP5-8.5 displaying the most severe drought fluctuations, whereas SSP1-2.6 and the basic condition exhibit relatively moderate variations. Overall, these findings highlight that SSP5-8.5 results in more intense and frequent drought conditions across all indices compared to SSP1-2.6 and the baseline scenario. The increasing trend of extreme drought events in high-emission scenarios emphasizes the urgent need for climate change mitigation strategies to reduce future climate risks and ensure water resource sustainability.

The graphs above illustrate variations in drought indices under different climate scenarios over time, including the SPI, which represents meteorological drought based on precipitation anomalies, the RDI, which incorporates both precipitation and PET to evaluate drought severity, and the eRDI, a modified version of RDI for enhanced sensitivity to changes in water availability. Each index is analysed under three conditions: the SSP1-2.6 scenario, representing a low-emission, sustainable development pathway with minimal climate change impact; the SSP5-8.5 scenario, a high-emission, fossil-fuel-driven pathway leading to more severe climate changes; and the baseline condition, serving as a historical reference for comparison. Observations reveal that SPI under both SSP1-2.6 and SSP5-8.5 fluctuates significantly, indicating variations in precipitation patterns, with SSP5-8.5 displaying more extreme drought and wet periods compared to the more stable conditions in SSP1-2.6. Similarly, RDI trends show increased drought intensity under SSP5-8.5, suggesting a rise in evapotranspiration due to higher temperatures. The baseline condition exhibits moderate drought variations, reinforcing the projected worsening of droughts under future climate scenarios. The eRDI further highlights this trend, with SSP5-8.5 showing the most pronounced drought severity, while SSP1-2.6 remains comparatively stable but still presents notable fluctuations. Overall, the findings suggest that higher emission scenarios lead to more severe and frequent drought conditions, emphasizing the need for climate adaptation strategies to manage future water resources effectively.

The graph above illustrates the trend of the GRI over time for three different piezometric wells: Airport well (orange line), Sarvak well (grey line), and Sharaf-Abad well (yellow line) from the year 2000 to 2023. The GRI, which represents groundwater availability, is observed to be declining consistently for all three wells, indicating a continuous depletion of groundwater resources over the studied period. Among the three wells, the Sharaf-Abad well shows the least negative values throughout the timeline, suggesting that its groundwater levels have remained relatively higher compared to the other two. Sarvak well follows a similar trend but with slightly lower values. Airport wells, on the other hand, exhibit the steepest decline, implying a more significant reduction in groundwater levels over time. Overall, the decreasing trend across all wells highlights the persistent depletion of groundwater resources, likely due to factors such as increased extraction, reduced recharge, and climatic changes. The findings emphasize the need for sustainable water management practices to mitigate further groundwater decline and ensure long-term water security in the region.

The graphs above depict the SSI values over time for the Sad-Abad station (Figure 8) and the Batary station (Figure 9), covering the period from 2000 to 2023. The SSI values indicate fluctuations in streamflow, with positive values representing wet conditions and negative values indicating drought conditions. In the Sad-Abad station (Figure 8), the initial years (2000-2006) show predominantly positive SSI values, signifying wetter conditions, with notable peaks reaching above 3.0. However, from 2007 onward, there is a shift towards negative SSI values, indicating persistent dry periods, especially between 2008 and 2020, where prolonged drought conditions are observed. Although some wet spells occur after 2017, the overall trend suggests increased dryness in the latter years. For the Batary station (Figure 9), a similar trend is observed, with the early 2000s experiencing relatively wet conditions, particularly between 2000 and 2006, where the SSI values are mostly positive. However, a transition to drier conditions occurs around 2007, with frequent and severe drought periods observed between 2008 and 2020, reaching an SSI of −3.0 in some years. Despite occasional wet phases, the streamflow variability suggests an overall declining trend in water availability. Comparing the two stations, both exhibit a noticeable shift from wetter to drier conditions over the years, emphasizing the impact of climate variability and potential water resource challenges in the region. These findings highlight the increasing frequency and intensity of drought events, which may require adaptive water management strategies to mitigate the adverse effects on water supply and ecosystems.

Validating climate models is essential to ensure their reliability in simulating historical climate patterns and predicting future changes. In this study, the performance of the HADGEM3-GC31-LL model was evaluated by comparing simulated temperature and precipitation data with observed records from the Dogonbadan and Yasouj stations. Statistical metrics, such as RMSE, MAE, correlation coefficient (R²), and NSE, were used to assess the accuracy of the model. This validation step helps determine the model's effectiveness in capturing regional climate variability, providing confidence in its projections under different SSP scenarios. Table 5 shows the result of climate model validation in two stations for temperature and precipitation.

This study aimed to assess the impact of climate change on meteorological and hydrological drought conditions in Kohgiluyeh and Boyer Ahmad provinces using multiple drought indices under two climate scenarios (SSP1.2-6 and SSP5.8-5). The results of this study provide new insights into future drought trends in the region by evaluating precipitation, temperature, groundwater levels, and streamflow data from 2000 to 2023 and projecting future conditions up to 2047.

The analysis of temperature and precipitation data indicates a projected increase in both parameters under both SSP scenarios, with SSP5.8-5 exhibiting slightly higher values than SSP1.2-6. This contrasts with many previous studies predicting a decline in precipitation in the region. For instance, studies by Labedzki (2006) and Loukas et al. (2008) suggested that climate change would lead to a decrease in precipitation and exacerbate drought conditions in arid regions. However, our findings align with research by Silva et al. (2007), which reported localized increases in precipitation despite global drying trends. The observed increase in precipitation in our study suggests that the study area may not experience intensified drought conditions despite rising temperatures. However, the relationship between increased precipitation and PET must be carefully considered, as higher temperatures can lead to increased water loss, potentially offsetting the benefits of higher rainfall.

The SPI, RDI, and eRDI were used to assess meteorological drought trends. The SPI results showed fluctuations in dry and wet periods over the historical period, with no significant changes under future climate scenarios. This suggests that the frequency and severity of meteorological drought events may not undergo drastic changes despite the expected increase in precipitation. Similarly, RDI and eRDI values remained relatively stable across all scenarios, implying that the influence of climate change on meteorological drought conditions in the region is minimal. These results contrast with findings by Mishra & Nagarajan (2011), who noted increasing drought frequency in similar arid and semi-arid regions. However, they are consistent with Asadi Zarch et al. (2011), who found that drought variability is influenced more by temperature-driven evapotranspiration than by precipitation alone.

Hydrological drought was assessed using the GRI and SSI. The GRI results indicate a persistent decline in groundwater levels over time, suggesting that despite stable meteorological drought conditions, groundwater resources are under significant stress. This is likely due to increased groundwater extraction and insufficient recharge rates. The declining trend in groundwater availability highlights the necessity for improved groundwater management strategies to prevent long-term depletion. The SSI results further emphasize hydrological drought concerns, with prolonged periods of low streamflow observed in both hydrometric stations. The most severe drought events were recorded in November 2009 and June 2018, demonstrating the region's vulnerability to streamflow reduction. These findings are in agreement with research by KhosraviDehkordi et al. (2019), who also reported declining groundwater levels in Iran due to both natural variability and human activities.

Comparing the findings across different drought indices, a key observation is the divergence between meteorological and hydrological drought trends. While meteorological drought conditions appear relatively stable, hydrological indicators suggest worsening water availability. This discrepancy highlights the importance of integrating both surface and groundwater assessments in drought studies to obtain a more comprehensive understanding of water resource challenges. Similar conclusions were drawn by Mohebbi Tafreshi et al. (2017), who emphasized the necessity of assessing groundwater and surface water trends separately to avoid misinterpretation of overall drought conditions.

The validation of the HADGEM3-GC31-LL model using statistical metrics confirmed its reliability in simulating historical climate conditions. The RMSE, MAE, NSE, and R² values indicated a reasonable agreement between observed and modelled climate data, reinforcing the credibility of the projected climate scenarios. These validated projections provide confidence in the study's conclusions regarding future drought trends.

Overall, this study underscores the complex nature of drought in Kohgiluyeh and Boyer Ahmad provinces. While meteorological drought conditions remain relatively stable, hydrological drought concerns are prominent due to declining groundwater levels and streamflow. Policymakers and water resource managers should prioritize adaptive water management strategies, including groundwater conservation, improved irrigation practices, and sustainable land-use planning, to mitigate the potential impacts of future droughts. Further research incorporating socio-economic factors and land-use changes would enhance the understanding of regional drought resilience and inform more effective mitigation strategies.

This study highlights the implications of climate change on drought conditions in Kohgiluyeh and Boyer Ahmad provinces, emphasizing both meteorological and hydrological drought assessments. While precipitation and temperature trends suggest relatively stable meteorological drought conditions, hydrological drought indicators, particularly groundwater depletion and reduced streamflow, signal increasing water resource challenges.

Given these findings, practical recommendations for water resource management include the implementation of integrated water management strategies to balance groundwater extraction and recharge rates. Authorities should promote water-saving irrigation technologies and alternative water storage solutions, such as rainwater harvesting and managed aquifer recharge systems. Additionally, stricter regulations on groundwater extraction and investments in monitoring systems for real-time data collection could aid in more effective drought mitigation.

Future research should explore the socio-economic dimensions of drought impacts, including agricultural productivity and rural livelihoods, to develop holistic adaptation strategies. Furthermore, an in-depth analysis of land-use changes and their influence on water resources would enhance understanding of long-term sustainability in the region. Incorporating high-resolution climate models and remote sensing techniques can also improve predictions and support more precise policy interventions.

In conclusion, while meteorological drought conditions remain relatively unchanged, hydrological drought is an escalating concern. Proactive water resource management and further interdisciplinary research are essential to mitigate the adverse effects of climate change on regional water availability and ensure long-term resilience.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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

The authors declare there is no conflict.