Assessment of droughts and extremes over India using CMIP6 simulations

The Indian Summer Monsoon (June–September, JJAS) plays a critical role in regulating water resources, agriculture, and hydroclimatic extremes across India, yet projecting future changes in monsoon variability remains challenging due to the coarse spatial resolution and biases of earth system models (ESMs). In this study, we develop high-resolution (0.25°) projections of monsoon rainfall over India using a statistical downscaling framework that combines weather typing with transfer functions, and we demonstrate how Standardized Precipitation Index (SPI) projections can be used to diagnose future changes in monsoon characteristics. Downscaled simulations from five GCMs are analyzed for a historical period and for two future socioeconomic pathways (SSP2-4.5 and SSP5-8.5). SPI is computed at monthly scale for June, July, August, and September, as well as for the seasonal JJAS total, enabling assessment of both intra-seasonal and seasonal hydroclimatic variability. Evaluation against observations shows that the historical simulations reproduce observed rainfall statistics with high fidelity, capturing both the mean and standard deviation across most regions of India. Furthermore, the downscaled GCMs successfully represent historical extremes, with more than 70% of grid cells capturing observed extreme drought events and over 80% capturing extreme wet events, providing confidence in the robustness of the derived SPI projections. Our next objective is to test the hypothesis that SPI contains discernible signals of key monsoon characteristics, including onset, withdrawal, and intraseasonal variability. If such signals are evident, SPI can serve as a useful diagnostic tool for inferring these features, which are otherwise difficult to predict directly. This framework further enables a range of analyses, including assessment of future projections and evaluation of shifts in the frequency, duration, and intensity of dry and wet spells during the monsoon season under both moderate and high-emission scenarios, thereby revealing changes in intraseasonal variability that may not be captured by seasonal mean rainfall alone.