Machine Learning–Enhanced Bias Correction of CMIP6 Data for Detecting Warming and Rainfall Shifts in Indian Hill Stations

Mountainous areas and the hill stations, which were traditionally considered cooler and with stable climatic conditions, are proving to mirror certain warming and changes in rainfall patterns. Considering the broader context of global climate change, this study investigates the presence of statistically quantifiable climatic shifts in the hill stations of South India by integrating observed IMD datasets with CMIP6 model simulations. An extensive bias-correction framework was employed to analyse and address the substantial systematic errors commonly associated with applying global climate models to complex terrain. The study combines established bias-correction techniques, including Quantile Mapping (QM) and Quantile Delta Mapping (QDM), with advanced machine learning algorithms such as CART, XGBoost, and a stacked ensemble model, enabling a more robust and comprehensive correction of model biases. XGBoost and the stacked model were the only approaches that demonstrated substantial improvements, showing reduced RMSE (0.55–0.76 for temperature and approximately 83–85 mm for precipitation), near-zero bias, and strong predictive skill (R² = 0.96 for temperature and NSE = 0.71 for precipitation). These models also achieved the lowest prediction uncertainty (RMSE) and the highest overall predictive performance (R²). The bias-corrected projections reveal pronounced warming across all the hill stations examined, aligning with recent evidence that traditionally cool regions are experiencing increased heat exposure. Rainfall forecasts indicate greater variability, suggesting a potential rise in both heavy rainfall events and prolonged dry spells. These findings strongly support the emerging understanding that the hill stations of South India are transitioning toward warmer and more climate-sensitive conditions. The study provides high-resolution, bias-adjusted datasets essential for climate impact assessments, tourism planning, ecosystem management, and the development of targeted adaptation policies to safeguard these vulnerable high-elevation environments.