Predicting changes in maximum temperatures in the mid-future period in Sistan and Baluchestan under SSP scenarios

Aim: Climate change, as a critical global issue, is primarily driven by human activities. This study aims to project changes in the mean maximum temperature across six meteorological stations in Sistan and Baluchestan province: Iranshahr, Chabahar, Zahedan, Zabol, Saravan, and Konarak. Projections were performed using CMIP6 models for the baseline period (1987–2014) and the middle future period (2051–2076).Material & Method: To forecast temperature changes, CMIP6 models were applied under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios using data from six meteorological stations. The SDSM method was used for statistical downscaling, and model performance was assessed with MAD, MAPE, and RMSE indices. The Mann-Kendall test analyzed trends in average maximum temperature. IDW interpolation in GIS was used to map spatial temperature changes. Paired-sample t-tests evaluated differences between baseline and mid-future periods.Finding: The CanESM5 model performed best in predicting temperature changes. The highest increase (31.2°C) occurred in July at Iranshahr under SSP5-8.5, while the lowest (0.63°C) was in Zahedan under SSP1-2.6. Annually, Iranshahr saw the highest rise (1.75°C) and Zahedan the lowest (1.34°C). Seasonally, summer at Iranshahr recorded the highest increase (2.24°C) under SSP2-4.5, and winter at Zahedan the lowest (0.82°C) under SSP1-2.6.Conclusion: The study predicts an increase in the average maximum temperature at all stations, with the highest rise in summer at Iranshahr and the lowest in winter at Zahedan. Geographical and climatic factors significantly influence temperature patterns. Continuous monitoring and improved management strategies are crucial for mitigating climate change.Innovation: This study utilizes advanced CMIP6 models to predict changes in the mean maximum temperature in Sistan and Baluchestan Province and provides spatial maps of these changes using the IDW interpolation method in a GIS environment. Additionally, it analyzes the relationship between temperature changes and local geographical and climatic conditions, offering valuable data for resource management planning and adaptation to climate change.