University of Birjand-Research Group of Drought and Climate Change Journal of Drought and Climate change Research 3092-6076 Articles in Press 2026 02 02 Prediction of Future Precipitation Changes under Climate Scenarios Using CMIP6 Models: A Study Based on the ACCESCM Model and Downscaling Analysis at the Saravan Station and Watershed Prediction of Future Precipitation Changes under Climate Scenarios Using CMIP6 Models: A Study Based on the ACCESCM Model and Downscaling Analysis at the Saravan Station and Watershed 3872 10.22077/jdcr.2026.10425.1185 FA Siavash Sepahian University of Sistan and Baluchestan Seyed Arman Hashemi Monfared University of Sistan and Baluchestan 0000-0001-5300-9808 Abbasali Ghaderi University of Sistan and Baluchestan Journal Article 2025 11 08 Climate change has become one of the greatest threats to human life. Gaining sufficient information about climate variations helps policymakers and regional managers—especially in arid and semi-arid regions—make better and more informed decisions.<br /> In this study, daily and monthly precipitation data from the Saravan meteorological station for the period 1987–2014 were used to simulate rainfall. Among the sixth assessment report (AR6) climate models evaluated for future precipitation prediction, the ACCESS-CM model was selected as the most suitable for the study area due to its high correlation (0.99) and low error (0.002) compared to other models. For downscaling the data, the Linear Scaling method was chosen because of its higher accuracy (RMSE = 0.002) and strong correlation (R = 0.99).<br /> Statistical tests indicated that only April exhibited a statistically significant rainfall trend at the 95% confidence level. Rainfall projections for future periods under different SSP scenarios suggest an increase in precipitation, particularly during winter and summer at the Saravan station. Overall, despite a general upward trend in precipitation under various SSP scenarios, the monthly and seasonal rainfall variations across the watershed area do not show statistically significant trends for future periods. Climate change has become one of the greatest threats to human life. Gaining sufficient information about climate variations helps policymakers and regional managers—especially in arid and semi-arid regions—make better and more informed decisions.<br /> In this study, daily and monthly precipitation data from the Saravan meteorological station for the period 1987–2014 were used to simulate rainfall. Among the sixth assessment report (AR6) climate models evaluated for future precipitation prediction, the ACCESS-CM model was selected as the most suitable for the study area due to its high correlation (0.99) and low error (0.002) compared to other models. For downscaling the data, the Linear Scaling method was chosen because of its higher accuracy (RMSE = 0.002) and strong correlation (R = 0.99).<br /> Statistical tests indicated that only April exhibited a statistically significant rainfall trend at the 95% confidence level. Rainfall projections for future periods under different SSP scenarios suggest an increase in precipitation, particularly during winter and summer at the Saravan station. Overall, despite a general upward trend in precipitation under various SSP scenarios, the monthly and seasonal rainfall variations across the watershed area do not show statistically significant trends for future periods.