Introduction: Climate change is one of the most pressing environmental challenges of the modern era, impacting natural resources and human livelihoods through rising temperatures, altered precipitation patterns, and increased frequency of extreme events such as droughts and floods. Due to its predominantly arid and semi-arid climate, Iran is particularly vulnerable to these changes. Evidence suggests that regions such as the northwest and the Zagros Mountains have experienced concurrent temperature increases and precipitation declines, a trend that is projected to intensify in the near future. This study aims to assess and visualize Iran’s climatic conditions over the next 20 years, quantifying the magnitude and extent of projected changes to inform national and regional planning efforts.
Materials and Methods: This research utilizes climate projections from the sixth generation of Coupled Model Intercomparison Project (CMIP6) under four greenhouse gas emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) for the period 2021–2040, comparing them with the historical baseline (1995–2014). In the first step, raw global climate model outputs were downscaled using the Change Factor (CF) method. Minimum and maximum temperature and precipitation data were extracted for both the baseline and future periods, and delta values were calculated for each variable. To correct biases and better capture local variability, observational data from the TerraClimate database (with a spatial resolution of approximately 5 km) were employed. Subsequently, predictive maps for minimum temperature, maximum temperature, and precipitation over the next 20 years were generated. To integrate these variables into a single metric, Weighted Linear Combination (WLC) was applied, where precipitation was assigned a higher weight through linear fuzzy membership functions. This approach yielded a composite index for assessing climate change intensity across the country. Finally, to compare historical and future trends, the climate change intensity map for the past 64 years was integrated with the projected two-decade map using an equal-weighted WLC framework.
Results: Findings indicate that in the next two decades, a significant increase in both minimum and maximum temperatures is inevitable across most parts of Iran. The projected rise may exceed 2°C for minimum temperature and 1.5°C for maximum temperature in some areas. Additionally, model outputs suggest a considerable decline in precipitation over parts of the Zagros region, the northwest, and critical watersheds such as Lake Urmia. This reduction may exacerbate soil dryness, decrease snowpack reserves, and deplete groundwater resources. The multi-criteria combined model identified the western and northwestern regions as the most severely affected by climate change, as they experience both significant warming and precipitation decline. This pattern is consistent with historical trends observed over the past 64 years, reinforcing the notion that warming and precipitation loss in these areas are part of a persistent, worsening trajectory. These results align with previous national studies and IPCC reports, emphasizing the urgent need for adaptation strategies and effective water resource management.
Discussion: This study highlights that in the coming decades, Iran will face heightened challenges related to increasing temperatures and declining precipitation. The warming trend and decreasing rainfall, particularly in the Zagros and northwestern regions, could have irreversible consequences for mountainous ecosystems, water resources, agriculture, and local livelihoods. Future research should incorporate dynamic downscaling models to provide a more detailed analysis of extreme climatic events and expand the station-based monitoring network to enhance data accuracy. Additionally, national and regional water and land management policies, public awareness campaigns, and greenhouse gas emission reduction strategies must be prioritized to mitigate the negative impacts of climate change in the years ahead.