Groundwater level forecasting is an essential priority for planning and managing groundwater resources. This study aims to investigate the effect of climate change on the monthly groundwater level in the Golpayegan aquifer in the future (2017-2032). After a spatiotemporal analysis, the LSSVR model was used to simulate the monthly groundwater level in the historical period (2002-2017). The input data included precipitation, temperature, pan evaporation, soil moisture (from the ESA CCA SM product), and groundwater level in observation wells on a monthly time scale. Future climatic data were downloaded from the CanEsm5 model of CMIP6 for the SSP1-2.6 and SSP5-8.5 climate scenarios and then downscaled using the Change Factor Approach (CFA). The spatial analysis of groundwater levels indicated four different behaviors in the observation wells in the Golpayegan aquifer, resulting in four different clusters using the AGNES clustering method. Historical and future period modeling were performed separately in each of the four observation wells from each cluster. The modeling in the historical period demonstrated an average of NRMSE (0.09), MBE (0.030), and R2 (0.94) for four clusters. The groundwater level in all clusters showed a decreasing trend in the future period, with SSP5-8.5 (average: 3.9 cm/month) showing a greater decrease than the SSP1-2.6 (average: 0.5 cm/month) scenario. The decline in groundwater level under SSP5-8.5 compared to the SSP1-2.6 were more, respectively, 4.8, 5.8, 9.9 and 3.7 meter for clusters 1 to 4. The results indicate the acceptable efficiency and accuracy of the LSSVR model results in evaluating the effects of climate change on groundwater levels.
