Evaluation of compound dry-hot extremes in summer monsoon in India: Past and Future

The intensification of the global hydrological cycle has significantly altered the behaviour of climate extremes. Lately, compound extremes i.e. simultaneous occurrence of two or more extremes, have received substantial emphasis because of their larger impact than individual extremes. Therefore, this study evaluates compound dry-hot extremes in summer monsoon season in India for the past and future using advanced statistical (variance transform method and REA- reliability ensemble averaging) and probabilistic methods (copulas). Monthly projections of precipitation/temperature are obtained from multi-modal ensembles of 21 CMIP5 GCMs, constructed using the REA technique. Furthermore, the frequency and spatial extent of monsoonal compound dry-hot events are assessed using a Standardized Compound Event Indicator (SCEI), based on monthly precipitation/temperature, for past (1975-2015)  and future (2025-2095 under RCP8.5). Moreover, vegetation loss estimates (using NDVI-Normalized Difference Vegetation Index) are evaluated under multiple dry-hot conditions (using SCEI) during 1982-2013 using bivariate copulas. Further, the teleconnections (Nino3.4, PDO, AMO and DMI) with SCEI are assessed using the variance transformation method. The results indicate a rise in dry-hot extremes in the monsoon season during 1975-2015. Due to the adverse impact of such extremes on vegetation, vegetation vulnerability assessment indicate that around 65.70% of the country’s area is prone to vegetation loss under extreme dry-hot conditions. And, it is also found that Nino3.4 (ENSO) is the dominant climate indice influencing SCEI (4 monthly scale), in > 50% of the country. Furthermore, the results show that the frequency and spatial extent of dry-hot extremes are projected to increase in the future (2055-2095), relative to past (1975-2015) across the country. Our study gives an enhanced understanding of dry-hot extremes in monsoon season and can further facilitate an effective adaptation strategy.