Objective: Soil moisture plays a significant role in agricultural production and climate dynamics. Given the increasing impact of climate change, this study aims to evaluate the capability of two selected CMIP6 models—ACCESS-CM2 and MPI-ESM1-2-LR—in simulating surface soil moisture and project this variable under two SSP scenarios (SSP1-2.6 and SSP5-8.5). This research addresses an important gap in projecting this variable in Qazvin Province.

Methods: Monthly surface soil moisture data from the ERA5-Land reanalysis database were used for the baseline period. The outputs of the two models were bias-corrected using the Linear Scaling (LS) method. To evaluate model performance, statistical indices such as RMSE, MAE, and the Pearson correlation coefficient were applied. Climate projections for the near future (2021–2040) were conducted under two SSP scenarios.
Results: The highest soil moisture levels occurred from January to April and from October to December, while the lowest values were observed in the central and southeastern regions. The ACCESS model outperformed the MPI model in representing spatial patterns, particularly in mountainous areas. The results indicated that under the SSP1-2.6 scenario, surface soil moisture would increase in winter, whereas a decrease or slight change was observed in summer. Under the SSP5-8.5 scenario, a more severe decline (up to 35%) occurred in summer and early autumn.
Conclusion: The findings suggest that soil moisture will decrease during warmer months due to climate change, potentially affecting irrigation management, drought monitoring, and agricultural crop yields.