The study aims to assess the climate change projections in South Gujarat region using bias-corrected General Circulation Model (GCM) projections under SSP245 and SSP585 scenarios. For maximum temperature, the chosen models were ACCESS-CM2, CMCC-ESM2, GFDL-CM4, KIOST-ESM, and TaiESM1. For minimum temperature, ACCESS-ESM1-5, CNRM-ESM2-1, EC-Earth3, and INM-CM5-0 were selected. The models identified for rainfall simulation included ACCESS-CM2, KACE-1-0-G, MPI-ESM1-2-LR, MRI-ESM2-0, and TaiESM1. These models were selected based on their accuracy in representing historical climate data and their applicability for future climate projections in the study region. Under SSP585, maximum temperature is projected to rise by 2.4 °C and minimum temperature by over 5.6 °C by the end of the century. Rainfall projections suggest a potential increase of up to 14.50% by 2090. An evaluation of GCM bias correction methods revealed that Quantile Mapping (QM) significantly outperformed Linear Scaling (LS) in reducing Root Mean Square Error (RMSE). While LS struggled with complex deviations, QM effectively corrected distributional biases and extreme outliers across temperature and precipitation datasets, proving essential for reliable climate modeling.