Evaluating Future Warming Scenarios in mainland India based on Bias-Corrected CMIP6 Multi-Model Ensembles

Understanding the spatio-temporal variability of temperature and its future evolution is critical for climate impact assessment and adaptation planning over India, a region characterized by complex physiography and pronounced climatic heterogeneity. This study presents a comprehensive assessment of future variations in monthly maximum, minimum, and mean temperatures over mainland India using outputs from Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models. To address climate model deficiencies, raw model outputs are bias-corrected and combined using an optimized multi-model ensemble framework. The resulting ensemble is applied to generate high-resolution temperature projections for the period 2025–2100 under four Shared Socioeconomic Pathway scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) to identify the most vulnerable regions for future warming. Spatio-temporal analyses reveal a consistent warming signal across the country, with the magnitude and spatial coverage of warming increasing markedly under higher-emission pathways. Regions characterized by complex topography, particularly the Himalayan and north-eastern areas, exhibit enhanced warming relative to the national average. Across all scenarios, monthly minimum temperature is projected to increase more rapidly than monthly maximum temperature, indicating intensified nocturnal warming and a progressive reduction in the monthly temperature range. These findings provide robust scientific evidence for anticipated temperature changes over India and offer valuable insights for climate risk assessment, policy formulation, and sustainable planning in climate-sensitive sectors.