Have general circulation models been created to simulate spatial patterns of climate variables in different regions of Iran? Do these models change with climate change? What are the conditions and characteristics of each? What is the future of Iran's climate according to the general circulation model?

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.

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),

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.