Malaysia experiences abundant rainfall, which can potentially lead to geo-hydrological disasters. Therefore, studying the effect of climate change on rainfall events is crucial. The General Circulation Model (GCM) is a well-accepted terrestrial-scale climate simulation approach widely employed by climate scientists and researchers worldwide. However, despite its comprehensive approach, GCM lacks in necessary precision at the local level due to its coarse resolution. Consequently, employing statistical downscaling techniques becomes essential for achieving accurate simulations at the local scale. Notably, there is a scarcity of localized studies focusing on the climate change effect, specifically in Penang Island. Penang Island was selected as the study area due to its high urbanization rate and frequent geo-hydrological disasters. The current study assessed the impact of climate change on mean annual rainfall (MAR) distribution using a statistical downscaling model (SDSM) under two representative concentration pathways (RCP4.5 and RCP8.5). SDSM is calibrated and validated, and rainfall spatial distribution maps are generated through Kriging and IDW methods for the observed (1990-2019) and future (2070-2099) periods. The results indicate that under both RCPs, MAR projections increased. RCP8.5 (14.93%) shows a higher effect, where the increment percentage is almost double that of RCP4.5 (8.6%). The model displays strong correlation and performance, with a disparity of 1.24% to 11.73%, averaging 7.50%, between observed and modelled results. The outcomes of this research hold significant implications for local authorities, providing valuable insights to enhance preparedness and response strategies concerning the evolving climate conditions, particularly in the context of geo-hydrological hazards, environmental concerns, and water security in Penang Island. However, it is crucial to acknowledge the study's limitation, considering only two climate scenarios (RCP4.5 and RCP8.5). Future research efforts should involve a broader spectrum of climate scenarios to yield a more comprehensive understanding of climate change's multifaceted and unpredictable nature for enhanced robustness of future climate-related strategies and policies.