Projected changes in precipitation and temperature over the KKSD river basin using a machine-learning-based selective CMIP6 multimodel ensemble

ABSTRACT Reliable climate projections are essential for climate-risk assessment and planning. Yet, uncertainty in Global Climate Models (GCMs) and ensemble construction remains a major challenge. This study developed a machine learning-based selective multimodel ensemble framework to project precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) over the Kangsabati, Keliaghai, Silabati, and Dwarkeswar (KKSD) river basin in eastern India. India Meteorological Department gridded observations were combined with statistically downscaled and bias-corrected NEX-GDDP-CMIP6 simulations. Multiple machine-learning algorithms were evaluated, and Extra Trees was adopted because of its strong predictive performance. A parsimonious four-GCM subset was evaluated against conventional multimodel approaches. Future projections for 2025–2098 under different SSP indicate an overall decline in annual precipitation, ranging from approximately −2.45% to −9.16% relative to 1991–2014, along with strong seasonal redistribution characterised by increased winter precipitation and persistent reductions in monsoon precipitation. Annual Tmax increases from approximately 0.39–0.44 °C in 2025–2039 to 1.53, 2.16, and 2.98 °C in 2085–2098 under SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively, while corresponding Tmin increases reach 1.40, 1.85, and 2.32 °C. The proposed framework therefore provides a parsimonious, explicitly evaluated approach to regional climate projection, while the projected climatic changes may have important implications for future water-resources planning in the KKSD basin.