Can the evaluation of atmospheric general circulation models in simulating spatial patterns of climate variables in different regions of Iran help climatology and climate changes in Iran and its neighboring regions? In general, do the climatic conditions of each geographical region depend on what climatic and synoptic patterns of atmospheric general circulation? How will scientific solutions help political and government experts and agencies related to climate, water, and drought?

Meteorological observations in Iran indicate a clear trend toward warming and drying. To assess how well current climate models capture these changes, we evaluated twenty CMIP6 General Circulation Models (GCMs) against observed spatial patterns of precipitation (Pr), minimum temperature (Tasmin), and maximum temperature (Tasmax) over 1987–2014. Model performance was quantified using the Kling–Gupta efficiency (KGE) and mean absolute error (MAE), leading to a ranking in which AWI‑CM‑1‑1‑MR and BCC‑CSM2‑MR emerged as the best performers. These two models were then used to project future climate under three Shared Socioeconomic Pathway scenarios (SSP126, SSP245, and SSP585) for the period 2060–2099. All scenarios indicate continued increases in both Tasmin and Tasmax and decreases in Pr, with the most severe changes under SSP585. AWI‑CM‑1‑1‑MR predicts a 4.1 °C rise in Tasmin, a 4.9 °C rise in Tasmax, and an annual precipitation decline of 7.1 mm, while BCC‑CSM2‑MR forecasts slightly more moderate shifts. Spatially, northern Iran faces greater temperature increases and rainfall reductions than the south. These projections underscore an urgent need for targeted adaptation measures to safeguard water resources, agriculture, human health, and other climate‑sensitive sectors in Iran.

Key words: climate change, CMIP6, general circulation models, Iran, precipitation, temperature.

INTRODUCTION: Climate change, one of the most pressing environmental crises of our era, has been increasingly threatening ecosystems, societies, and the global economy (Lungarska & Chakir 2024; Zhang, T. et al. 2024). This phenomenon, which is known for a sustained change in worldwide or regional weather patterns, manifests in a variety of forms, including an increase in temperature (Tsai et al. 2024), a transformation in precipitation (Pr) patterns (Sun et al. 2024), the occurrence of extreme weather events, and an elevation in sea levels (Doorga et al. 2024). The key reason for climate change is the substantial rise in atmospheric greenhouse gas concentrations (Shah et al. 2024). It has disturbed the Earth’s energy balance and led to adverse global environmental, social, and economic consequences (Ding et al. 2024). Along with the alteration in Pr patterns and temperature, the rise in greenhouse gas concentrations has accelerated the frequency, duration, and severity of climate-change-related events (Hosseini et al. 2020; Sharafati & Pezeshki 2020). It is of great importance to gain an understanding of how hydrological processes are affected by climate change and to measure the uncertainty related to the hydrological response at a regional scale (Bekele et al. 2019). The intensification of the Earth’s climate dynamics has led to significant alterations in the equilibrium of the Earth’s system. Consequently, the frequency and intensity of floods, heat waves, droughts, and ecosystem disturbances are increasing. In Earth’s dynamic environment, a comprehensive and accurate understanding of climate dynamics and the ability to forecast changes are essential to guarantee a sustainable future. Meanwhile, atmospheric general circulation models (GCMs),highly sophisticated computational tools, have made notable advances in this field (Xue et al. 2024). These intricate mathematical models, which simulate the physical processes that govern air and ocean circulation, provide insight into the Earth’s climate system, offering a more profound comprehension of its complex dynamics. By incorporating factors such as radiation transport, heat exchange, and moisture transfer within three-dimensional grids, GCMs can simulate largescale displacements of air and water masses (Peng et al. 2020). The simulations provide a comprehensive picture of climate patterns at the global level, which is vital for studying climate change and formulating strategies to deal with it (Illangasingha et al. 2023). While GCMs possess considerable capabilities, their performance is not without constraints. The limited spatial resolution of these models prevents comprehensive documentation of minor climatic events or regional transformations (Guo et al. 2023). However, Using representative concentration pathways, GCMs project future climate changes that provide critical insights for large-scale planning and policy decisions at national and global levels. There is no doubt that GCMs are indispensable for understanding and predicting the climate system’s behavior under different greenhouse gas emission scenarios (Sun et al. 2023). Such models enable scientists to study the intricate dynamics and processes that drive climate change. However, selecting the most suitable GCMs for a particular region represents a significant challenge, given the diverse range of existing models with varying degrees of strength and weakness (Raju & Kumar 2020). This challenge can be due to various factors. For example: (1) Inherent uncertainty in GCM models: These models are formulated based on complex mathematical equations and different assumptions, which result in an inherent uncertainty in their outcomes. The selection of an appropriate model necessitates an understanding of and ability to manage these uncertainties (Zhang, B. et al. 2024). (2) Limitations of GCM spatial resolution: GCMs are generally for global or continental scales that may not afford sufficient spatial resolution to record local climate features accurately. It is crucial to select a model with an appropriate spatial resolution for the target area (Benedict et al. 2017). (3) Variance in model performance: the performance of different GCM models in predicting weather variables, such as Pr, temperature, and wind, varies. It is, therefore, necessary to select a model that can perform well for the intended area (Hodnebrog et al. 2022). The selection of an appropriate model necessitates a comprehensive grasp of the mentioned challenges and their effective management. Iran’s diverse climatic conditions are undergoing profound transformations due to global climate change, marked by increasing temperature extremes and erratic Pr patterns, exacerbating water scarcity. The country’s topography, comprising arid and semi-arid regions, renders it particularly susceptible to the impacts of weather anomalies (Abedi Sarvestani & Millar 2024). Recent studies have highlighted the necessity to address the rising frequency and intensity of droughts, which present significant challenges to the sustainability of agriculture, water resources management, and economic and social stability. The need for robust climate adaptation strategies is underscored by predictions of continued warming and Pr variability, requiring interdisciplinary research and policy integration to reduce negative impacts and strengthen resilience to cope with uncertain climate futures (Pakrooh & Kamal 2023). This research focuses on the performance evaluation of GCM in the spatial pattern simulation of Pr, minimum temperature (Tasmin), and maximum temperature (Tasmax) across Iran for the period of 1987 to 2014. A set of GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) archive has been nominated according to global performance and availability. The Kling–Gupta efficiency (KGE) and mean absolute error (MAE) were employed to assess the model’s performance in simulating spatial patterns of climate variables. KGE evaluates the model’s ability to accurately capture the observed variability, mean, and correlation. MAE provides a measure of the average magnitude of the errors between the simulated and observed values, offering insights into the model’s systematic and random errors. Iran is currently facing critical challenges of water scarcity, and climate change projections indicate that the situation will worsen (Talebi 2023). Accurate predictions of future temperature and Pr patterns are essential for developing effective water management strategies and climate adaptation programs. This study attempts to contribute to this important endeavor by providing a framework for selecting GCMs that produce reliable climate change predictions for Iran. The area of the study Iran is a large country in the west of Asia with an area of about 1.65 million square kilometres. It lies between 44° and 66°east longitude and 25° and 40° north latitude, with a wide range of climates and topographies. Iran’s altitude varies from 5,500 m in the western mountains to 24 m below sea level on the northern coasts. There are heavy rains and low temperatures in the high mountainous areas. As the temperature rises from northwest to southeast, Pr gradually declines. Iran has eight climatic zones according to Pr patterns. Modarres (2006) identified eight Pr regions using the hierarchical clustering technique based on monthly and annual rainfall data from 28 stations scattered throughout the country. Table 1 describes the general conditions of each region, and Figure 1 shows the spatial distribution of each region on the map of Iran. The study describes these eight regions and their Pr patterns. About 50% of the area of Iran has a semi-arid and arid climate (G1). Two extensive deserts, Dasht-e Kavir and Lut, lie in this area. In addition to G1, G2, G3, and G4 are the main climate zones. The four zones (G1–G4) comprise about 89% of the country and reflect the climatic diversity of Iran. The annual Pr variability is significant in the northwestern and the northern high-Pr zones (G3, G6, and G8); therefore, droughts are more common in these regions. Table 2 shows the entire list of meteorological stations in the study areas. This study used monthly temperature and Pr data from 1987 to 2014 to evaluate atmospheric GCMs and predict droughts in the near and distant future. Data used This study employs a combined approach using both CMIP6 climate models and observational data from meteorological stations to investigate climate change patterns in Iran. Observational data, spanning from 1987 to 2014, were obtained from the Meteorological Organization and the Ministry of Energy and Water Resources to provide a historical context for model evaluation and future projections. Twenty climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) are utilized in this study. CMIP6 is an international program that develops and compares climate models from different research institutions to predict future climate change (Table 3). The data used by the models were derived from the Earth System Grid Federation (ESGF) online system. Two main conditions were used to select them: (1) availability of monthly simulations and (2) availability of data for three emission scenarios of the Shared Socioeconomic Pathways (SSPs). The Intergovernmental Panel on Climate Change developed these scenarios to assess the prospect of greenhouse gas emissions: (1) SSP126, known as the ‘Sustainable World’ or ‘Green World,’ represents robust global cooperation for sustainable development, social equity as well as environmental protection. In this scenario, emissions of greenhouse gases are reduced significantly, and global warming is limited to 1.5 °C (Li et al. 2024). (2) SSP245 represents moderate progress in sustainable development and environmental protection. In this scenario, greenhouse gas emissions decline, and global warming reaches 2 °C (Ru et al. 2024). (3) SSP585 focuses on economic growth that relies on fossil fuels and unsustainable development paths. In this scenario, greenhouse gas emissions increase significantly, and global warming exceeds 4 °C (Das et al. 2024). All climate models utilize the same initial conditions (r1i1p1) for a fair comparison. These conditions include the group members (r1), the initial conditions (i1), and the physical parameters (p1). A monthly timescale was chosen for evaluation to ensure consistency with the intended application of the topperforming GCMs for long-term forecasting. Moreover, given that the observational data employed in this research were aggregated monthly, aligning the timescales of both the model outputs and observations was deemed essential for a robust comparison. To address the spatial mismatch between the meteorological station locations and the GCM grid points, a bilinear interpolation method was employed. This method involves calculating the weighted average of the four nearest grid points to the station location. Specifically, for each station, the four grid points forming a rectangle enclosing the station were identified. Using the latitude and longitude coordinates of the station and the four grid points, bilinear weights were calculated to determine the contribution of each grid point to the interpolated value at the station location. The interpolated value was then calculated as a weighted average of the values at the four grid points. METHODOLOGY This research applied a retrospective method to select and rank the most appropriate atmospheric GCMs to investigate climate change in Iran. It is based exclusively on the measured performance of the model over historical periods (Jeong & Cannon 2023). This method evaluates the historical performance of the models on annual and seasonal scales. The criteria applied were the KGE and MAE, which measure distinct aspects of model accuracy, including the simulation of observed values, deviation from observations, and overall error magnitude. The following steps were executed separately for each criterion: (1) Calculation of KGE and MAE: both metrics were computed to assess model performance. (2) Derivation of Performance Indicators: the mean and standard deviation of KGE and MAE values were calculated. (3) Integration via general performance index (GPI): the GPI relationship – which quantifies GCM performance in simulating minimum temperature (Tasmin), Tasmax, and Pr – was used to unify performance indicators across timeframes. (4) Ranking of GCMs: after calculating GPIs for all GCMs and variables (Tasmin, Tasmax, Pr), models were ranked based on their GPI scores, with higher GPIs corresponding to better ranks. (5) The bottom 14 models (ranked 7–20 out of 20 total GCMs) were excluded for any variable, retaining only the top six GCMs (ranked 1–6, representing the top 30% of models). (6) Final GCM Selection: the remaining models were re-ranked, and final GCMs were identified for each criterion. Common GCMs across both criteria were retained for further analysis. (7) Projection of Climate Changes: using the selected GCMs, future changes in Pr, minimum temperature, and Tasmax were projected for the near term (2020–2039), mid-term (2040–2059), and long term (2060–2099) under the SSP126, SSP245, and SSP585 scenarios. These projections were mapped to reflect Iran’s evolving climate spatial patterns (Figure 2). GCMs evaluation method This study uses two valid criteria to measure the atmospheric GCM’s performance in simulating the climate of Iran. The indices are: (1) KGE Index: KGE is a statistical criterion used to evaluate the correlation in variability and mean of the observational and simulative data. This criterion is calculated annually for different GCMs and different variables. Gupta introduced this criterion in 2009 and expressed it as follows:whereas r represents the Pearson correlation coefficient between the simulation data (S) and the observational data (O), γ indicates the bias value of the simulated data, β shows the magnitude of the changes in the simulated data, and s and μ are the standard deviation and the mean values, respectively. The KGE metric, ranging from negative infinity to 1, serves as a robust tool for evaluating the performance of GCMs in simulating hydrological processes. A KGE value of 1 signifies a perfect match between simulated and observed data, whereas values closer to zero indicate poorer model performance (Knoben et al. 2019; Ma et al. 2020; Sanchez Lozano et al. 2025).(2) MAE index: it is a metric of the error between the observational and simulated data, its scale is the same as the data and it is not for data with different scales. This criterion is defined as follows:where y and x are the simulated and observed data, respectively, calculated for each data (i) over the total data (n). According to the above equation, the closer MAE is to zero, the better the model performs. This metric measures the average difference between the model’s simulated and actual values. MAE is always non-negative; the lower this value, the more accurately the model simulates the actual values (Yuan et al. 2024).The way these metrics work is that for each GCM model, simulated values are then compared with actual data over the evaluation period (1987–2014). Then, for each model, year, and season, KGE and MAE values are calculated. Finally, these values are compared for different models and at various time and space intervals. Models with the highest KGE and lowest MAE are considered the best for simulating Iran’s climate. GCMs ranking procedure Ranking GCMs is challenging due to their varying performance across different climate variables (e.g., temperature, Pr) and temporal scales (annual, seasonal). To address this, the generalized performance index (GPI) – a multicriteria ranking approach – was applied to systematically evaluate, rank, and filter GCMs. The GPI integrates two statistical measures (mean and standard deviation) derived from two criteria (KGE and MAE) across five temporal scales (annual, winter, spring, summer, autumn). For each GCM and climate variable, ten performance indices (two measures five scales) were normalized and consolidated into a single GPI value. This index quantifies the deviation of a GCM’s normalized performance from the median value across all models. A higher GPI indicates superior model performance relative to the ensemble median. The calculation of the GPI for a particular climate variable can be as follows: GPIj ¼ X 10 j¼1 aj(yej yij) (5) GPIj: Represents the overall performance index of the ith model. A higher value of GPIi indicates better model performance. aj: The value of the coefficient aj for each performance index is considered to be either 1 or 1. This value is determined empirically based on the nature of each index. For indices that indicate model accuracy (such as the mean), a positive sign is used; this is because higher values of these indices indicate better model performance. Conversely, for indices that indicate model dispersion or noise (such as standard deviation), a negative sign is used; this is because lower values of these indices indicate better model performance. ỹj: Represents the mean of the jth performance index across all models. yij: Represents the value of the jth performance index for the ith model. The second step involves eliminating underperforming models and final ranking, structured as follows: (1) Elimination of weak models: models ranked in the bottom 66% for any variable (maximum temperature, minimum temperature, or Pr) were excluded. This ensures only models capable of accurately simulating all three variables advance, as comprehensive climate change assessment requires proficiency across Pr and temperature extremes. (2) Final ranking: the remaining models were ranked by summing their individual ranks across the three variables. A lower total rank indicates superior overall performance and reliability. The third step identifies models common to both criteria (MAE and KGE). These shared models, excelling in both accuracy and error metrics, are deemed the most reliable for simulating Iran’s climate. RESULTS Evaluation of the models based on the KGE This study investigated the performance of 20 atmospheric GCMs in simulating Pr, Tasmax, and Tasmin, from 1987 to 2014. The results showed that the performance of the models in simulating these climate variables was significantly different. As shown in Figure 3, the CIESM models performed poorly in simulating annual Pr, while the EC-EARTH3 and EC-EARTH3-veg models performed better. These models were more accurate in simulating not only annual Pr but also seasonal Pr patterns. The data in Figure 4 shows that for maximum temperature, the EC-EARTH3 and EC-EARTH3-veg models performed poorly in the annual Tasmax simulation, while the AWI-CM-1-1-MR, INM-CM4-8, and INM-CM5-0 models had acceptable performance. These models simulated very accurately the seasonal pattern of Tasmax. For the Tasmin, Figure 5 shows that the MIROC6 and NESM3 models performed poorly in simulating annual values. However, the AWI-CM-1-1- MR and BCC-CSM2-MR models performed better and could simulate seasonal Tasmin values and patterns with acceptable accuracy. Overall, GCMs exhibited poorer performance in simulating winter Pr compared with the other seasons based on the KGE metric. For Tasmax, GCMs demonstrated the best performance in simulating maximum winter temperatures, while spring simulations were the weakest. Regarding Tasmin, GCMs performed very well in winter simulations but showed average performance during spring and summer.Evaluating models based on MAE The evaluation of the models showed that the performance of the models in simulating these climate variables was significantly different. Figure 6 shows that some models, such as CIESM and CMCC-ESM2, performed poorly in the annual and seasonal Pr simulation, while BCC-CSM2-MR, EC-Earth3-VEG, and AWI-CM-1-1-MR performed better. For maximum (Tasmax) temperature, the AWI-CM-1-1-MR model performed very well, while NESM3 was the weakest model (Figure 7). The MIROC6 and NESM3 models had poor performance for Tasmin simulation, while EC-Earth3-veg had acceptable performance (Figure 8). The poor performance of some models in simulating climate variables may be due to several reasons, including incomplete parameterization of related processes, mismatch of model input data with reality, or errors in the model code. On the other hand, the better performance of some models may be due to more accurate parameterization, better fitting of input data, or the presence of more efficient algorithms in the model code. Overall, GCMs exhibited reasonable performance in simulating Pr across all seasons based on the MAE metric. However, when considering Tasmax, GCMs demonstrated satisfactory performance only during the summer months, while their performance in the other seasons was less favorable. In contrast, GCMs consistently performed well in simulating Tasmin across all seasons, with particularly excellent results during the summer.GCMs ranking After calculating the KGE and MAE indices from 1987 to 2014, the mean and standard deviation are computed for both criteria and five separate periods. It is important to note that an efficient GCM will have KGE values close to 1 and MAE values close to 0 when estimating variables. Also, the minor standard deviation for both indicators is an indication of the efficiency of that model.As can be seen in Figures 9 and 10, the performance of most of the GCMs is generally favorable. However, despite the appropriate mean, they show a weaker performance in estimating autumn Pr. The high standard deviation of the KGE for autumn Pr is evidence of this poor performance in the mentioned timeframe. In contrast, the GCMs were much better at estimating Tasmax and Pr than minimum temperature. The calculated mean and standard deviation values are normalized from 0 to 1 in the next step. The GPI method then converts these ten performance indices (including the mean and standard deviation of the KGE and MAE for each season) into a single index showing the overall performance of each GCM. The performance indices were normalized to a common scale to ensure fair comparison. Table 4 shows the median of the normalized indicators. Table 5 shows the calculated value of the GPI for all GCMs. After calculating the GPI index for each model, they are ranked based on these values. The first position goes to the model with the highest GPI value. For example, the AWI-CM-1-1-MR model received ranks 4, 1, and 1 for the Pr, Tasmin, and Tasmax variables, respectively, in the KGE criterion. Figure 11 details this ranking. After ranking, the models with a rank lower than 66% (rank 14 and below) in estimating either of the two variables (indices) are eliminated. A model may perform very well at estimating one parameter but poorly at estimating another. Since accurate estimation of both variables is critical, such models are discarded. The final ranking is based on the sum of the ranks of each GCM in both criteria. The best models are then determined. The final ranking is presented in Table 6. Based on the composite ranking, the AWI-CM-1-1-MR model emerged as the top performer. This model achieved the lowest overall rank, indicating superior performance across all variables and metrics. It demonstrated particular strength in simulating maximum and minimum temperatures, as evidenced by consistently low ranks in the Tasmax and Tasmin columns. The BCC-CSM2-MR model secured the second position. Although it did not outperform the AWI-CM-1-1-MR in all categories, it consistently showed competitive performance across all variables. Its relatively low ranks in both KGE and MAE metrics highlight its ability to effectively capture observed climate patterns.Table 5 | Performance indices of the atmospheric GCMs for the simulation of temperature and Pr GCM KGE_GPI MAE_GPI Pr Tasmin Tasmax Pr Tasmin Tasmax ACCESS-CM2 1.2982 0.9313 3.8251 0.3253 0.4824 1.4220 AWI-CM-1-1-MR 1.4874 3.0144 9.2985 0.8305 0.6617 5.4962 BCC-CSM2-MR 1.2185 1.2846 0.6934 1.4750 0.3434 0.6598 CAS-ESM2-0 4.3358 0.8018 2.0109 3.7035 0.3124 0.3095 CIESM 0.0268 0.2960 3.6436 3.6572 0.3668 0.3914 CMCC-ESM2 0.4197 3.1532 0.7462 0.7096 0.9831 0.9574 EC-Earth3 0.0592 2.3460 6.7044 0.3899 1.3965 0.7994 EC-Earth3-veg 2.8197 0.2344 5.0281 1.4160 0.9247 0.6786 ESM1-5 1.7218 0.4000 0.7724 0.5259 0.3268 1.4218 FGOALS-g3 1.4053 0.1701 3.2570 0.8427 0.0767 0.0821 FIO-ESM-2-0 1.6513 3.3359 0.6184 0.4270 1.4625 0.8537 GFDL-ESM4 1.4678 1.6025 4.0150 0.1950 0.8920 1.2714 INM-CM4-8 2.8274 2.5483 5.4052 1.2624 1.0249 2.6188 INM-CM5-0 2.1962 2.9106 4.8150 0.5586 0.0186 2.8455 IPSL-CM6A-LR 0.1676 2.0228 4.1279 1.0934 1.0219 2.1587 MIROC6 2.0227 10.5520 1.4459 0.8841 6.7630 2.4287 MPI-ESM1-2-HR 2.1332 2.7240 0.5446 0.9209 0.4909 0.3501 MPI-ESM1-2-LR 0.9548 2.3672 3.4456 0.2459 0.4786 0.7594 MRI-ESM2-0 7.1476 1.5545 6.4456 4.0769 0.1854 1.7079 NESM3 0.1182 10.2588 4.8669 0.3221 6.6868 1.8954. Table 6 | Performance ranking of climate models for three climate variables KGE MAE Tasmax Tasmin Pr Tasmax Tasmin Pr AWI-CM-1-1-MR 1 1 1 1 2 3 BCC-CSM2-MR 4 2 2 3 3 1 EC-Earth3-veg - -- 4 12 CIESM 2 3 3 - -- INM-CM5-0 - -- 2 44 FGOALS-g3 3 4 4 - --Based on the results presented in the tables above, the AWI-CM-1-1-MR and BCC-CSM2-MR models have received high ratings in both methods and are selected jointly as the final models. Projected changes in Pr, Tasmin, and Tasmax Analyzing future climate scenarios reveals alarming trends in both temperature and Pr. Changes in Tasmin and Tasmax and Pr for three future periods (2020–2039, 2040–2059, and 2060–2099) relative to the reference period (1987–2014) were studied using two selected models from the atmospheric GCMs named AWI-CM-1-1-MR and BCC-CSM2-MR and for three climate scenarios SSP126, SSP245 and SSP585. The results obtained from both models consistently show an increase in maximum and minimum temperatures (Tasmin and Tasmax) and a decline in Pr. However, there are some critical differences between the two models. Figure 12 | Predicted changes in three time-periods based on three scenarios for the top two models . The AWI-CM-1-1-MR model predicts a more significant increase for Tasmax than for Tasmin, while the BCC-CSM2-MR model suggests a more balanced increase for both values. Also, the Pr reduction in the AWI-CM-1-1-MR model is significantly higher than in the BCC-CSM2-MR model. Considering specific scenarios, for the near future (2020– 2039), the SSP126 scenario predicts minimum temperatures to increase by 1.8 °C, maximum temperatures to increase by 2.3 °C, and Pr to decrease by 4.8 mm. For the long term (2060–2099), the scenario projects that the minimum temperature will rise 2.2 °C, the Tasmax will increase 2.6 °C, and Pr will fall 5.0 mm. Similarly, the SSP585 scenario for the near future shows a 2.1 °C increase in minimum temperature, a 2.3 °C increase in maximum temperature, and a 5.1 mm decrease in Pr. However, the long-term projections for the SSP585 scenario are much more extreme, predicting a 4.1 °C increase in minimum temperature, a 4.9 °C increase in maximum temperature, and a 7.1 mm decrease in Pr (Figure 12). In summary, future climate scenarios indicate an overall trend of increasing temperatures and decreasing Pr. The pace of these changes appears to be accelerating in the long term, highlighting the potential for significant climate change in the coming decades. For the two models mentioned above in Iran, Figures 13 and 14 show the percentage changes of the variables for different scenarios and the AWI-CM-1-1-MR and BCC-CSM2-MR models, respectively. The figures show that Tasmin and Tasmax increased, while Pr decreased for both models. Most changes occur in northwestern Iran, gradually decreasing toward the southeast. For example, the increase in Tasmax in each time-interval will be more significant in the northwest than in the southeast. These graphs also show that the lowest changes in Tasmin and Tasmax occur in the areas around the Persian Gulf. Based on the graphs related to Pr, the projection is that there will be a slight increase in Pr only in the southwest of Iran, and the other regions will face a decrease in Pr. The comparison of the two models shows that both models provide similar results in predicting parameters in different periods and scenarios. However, the amount of change in the AWICM-1-1-MR model is more significant and evident. The images indicate that central Iran will experience substantial changes in temperature and Pr. Minimum and maximum temperatures, especially in the central and eastern parts, are set to rise, Figure 14 | Predictions based on the BCC-CSM2-MR model for the three variables of (a) minimum and (b) maximum temperature and (c) Pr.

while Pr is expected to decline. These trends are projected across all scenarios, with more pronounced changes under high-emission scenarios. DISCUSSION The evaluation of 20 CMIP6 GCMs through the KGE and MAE metrics underscores significant variability in their ability to replicate observed climate patterns over Iran. The superior performance of the AWI-CM-1-1-MR and BCC-CSM2-MR models aligns with prior studies: for instance, Shiru & Chung (2021) highlighted the AWI-CM-1-1-MR’s proficiency in simulating Tasmin in Nigeria, and Zabihi & Ahmadi (2024) identified the BCC-CSM2-MR as a top-performing model for Iran. This consistency across diverse regions reinforces the reliability of these models for regional climate assessments. The projected climate trajectories under SSP scenarios reveal alarming trends: a persistent rise in Tasmin and Tasmax alongside declining Pr. These findings align with broader patterns of warming and aridification documented in Southwest Asia, as noted by Babaeian et al. (2024). The spatial heterogeneity of these changes – intensified warming in northern Iran and milder shifts in the south – reflects the complex interplay of topography, atmospheric circulation, and land–surface feedback. Notably, the AWI-CM-1-1-MR model predicts more severe temperature and Pr anomalies than BCC-CSM2-MR, suggesting inherent uncertainties in emission scenario projections and model physics. Such discrepancies require ensemble approaches to risk assessment, as reliance on single-model projections may underestimate or overstate climatic extremes. The spatial granularity of projected changes underscores Iran’s vulnerability to climate-driven disruptions. Northwestern Iran, a critical agricultural zone, faces the most pronounced temperature increases, threatening crop yields and water availability. Conversely, the southeast’s relatively muted warming may mask compounding risks, such as intensified dust storms and groundwater depletion. Pr declines in central and eastern regions, contrasting with marginal increases in the southwest, mirror patterns observed by Majdi et al. (2022), who attributed such variability to shifting subtropical jet streams and monsoon dynamics. These regional disparities complicate adaptation planning, as uniform policies may fail to address localized vulnerabilities. The socio-economic ramifications are profound. Projected temperature rises of 4.1 °C (Tasmin) and 4.9 °C (Tasmax) under SSP585 by 2099, coupled with a 7.1 mm annual reduction in Pr, will exacerbate water scarcity, particularly in basins reliant on snowmelt. Agriculture, a cornerstone of Iran’s economy, faces dual threats: heat stress on staple crops and reduced irrigation capacity. The health sector, too, must prepare for increased heat-related morbidity and expanded vectorborne disease ranges, as noted in Francis & Fonseca (2024). These cascading impacts demand integrated adaptation strategies, spanning water-efficient technologies, drought-resistant crops, and heat-resilient urban planning. CONCLUSIONS The AWI-CM-1-1-MR and BCC-CSM2-MR models demonstrated superior performance in simulating Iran’s historical climate variables, making them robust tools for regional climate projections. All SSP scenarios project a continuous rise in Tasmin (4.1 °C) and Tasmax (4.9 °C) alongside a 7.1 mm decline in Pr by 2099, signaling a transition toward a hotter, drier climate regime. Northwestern Iran will experience the most severe temperature increases, while Pr declines will disproportionately affect central and eastern regions. The findings underscore the urgency of implementing adaptive measures in agriculture, water management, and public health to mitigate socio-economic disruptions.

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