<p>Compared to observed data, this study assesses the accuracy of CSIRO-Mk-3–6-0 model climate data in predicting climatic events in Punjab, India. It aims to minimize bias in rainfall and temperature data by computing correction factors using 2010–2017 datasets and their validation using 2018–2020 datasets by statistical analysis. The study evaluated six rainfall corrections (Basic Quantile Mapping (BAQM^BC), Modified Quantile Mapping (MOQM^BC), Normal Mapping (NOM^BC), Gamma Mapping (GAM^BC), Quantile Mapping linear correction (QMLC^BC) and QM second-order polynomial correction (QMPOL^BC)) and three temperature correction (Simple Seasonal Bias Correction (SSBCM^BC) monthly basis, Change Factor (CFD^BC) daily basis and Nudging (BCD^BC) methods. The results were analyzed with descriptive statistics, cumulative distribution function (CDF) plots and the Kolmogorov–Smirnov (KS) non-parametric test. The CDF plots and KS tests show both significant distribution differences (QMLIN^BC and QMPOL^BC) and non-significant distribution differences (BAQM^BC, MOQM^BC, NOM^BC, GAM^BC) for rainfall parameters compared to raw model data and bias-corrected data compared to observed data. The bias-corrected rainfall data did not achieve a closer distribution alignment with the observed as indicated by the evaluation metrics (RMSE, NRMSE, SD and mean) across five study locations. In temperature datasets, the CFD^BC technique outperformed SSBCM^BC and BCD^BC as indicated by the excellent statistical evaluation metrics.</p>