Near Future Projections of Indian Summer Monsoon Rainfall Extremes Using QDM bias corrected CMIP6 Multi-Model Ensemble under Climate change Scenarios

Anthropogenic influences and ongoing climate change are fundamentally reshaping the global hydrological cycle, manifesting in heightened frequency and severity of extreme precipitation events. Delivering robust projections of these changes is imperative for effective adaptation and risk mitigation, particularly in monsoon-dependent regions such as India.
This investigation undertakes a rigorous assessment and near-term projection (2015–2050) of Indian Summer Monsoon Rainfall (ISMR) extremes through an ensemble of 19 CMIP6 global climate models. A comprehensive multi-metric evaluation protocol was established, integrating nine distinct statistical and distributional indices—including measures of bias, variability, spatial congruence, and distributional fidelity—to systematically benchmark model performance evaluated against high-resolution IMD observational datasets. The ten most skillful models were identified and subsequently subjected to Quantile Delta Mapping (QDM) bias correction, an advacned bias correction approach that harmonizes modelled rainfall distributions with observations while preserving projected climate change signals.
The QDM-adjusted multi-model ensemble facilitated projections of mean and extreme ISMR under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Bias correction via QDM markedly enhanced model integrity, substantially reducing systematic discrepancies and improving representation of observed interannual variability and extremes. Projections indicate a pronounced amplification of ISMR extremes, with particularly robust signals over the South Peninsular, Hilly, and West-Central regions, alongside notable spatial heterogeneity.
These results underscore the critical importance of rigorous model selection and advanced bias-correction methods, such as QDM, for generating credible climate projections. The study provides essential insights to inform water resource management, disaster risk reduction, and policy frameworks addressing heightened hydrometeorological risks in a warming climate.