Rainfall extremes on the rise: Observations during 1951–2020 and bias-corrected CMIP6 projections for near- and late 21st century over Indian landmass

In this study, we provide a comprehensive analysis to identify and quantify spatial patterns of heavy, very heavy and extremely heavy rainfall and their trends that have emerged during the last seven decades (1951 to 2020) of monsoon months (June to September) under warming scenario as well as to project these extreme rainfall counts during the near- (2036–2060) and late-21st century (2075–2099) w.r.t. historical period (1990–2014) using bias-corrected Coupled Model Intercomparison Project-6 (CMIP6) multi-model ensemble method. On the basis of daily maximum rainfall occurrences, the Central India, North-East India, Western Ghats and Eastern Ghats are found to be susceptible to extreme rainfall zones over the Indian landmass. The trend distribution during 1951–2020 suggests an increase of 42–63 heavy rainfall events over Orissa, Chattisgarh and parts of Madhya Pradesh and a declination over Uttar Pradesh, Kerala and hilly regions of North-East India. Our study suggests the causal theory for the rise of monsoon rainfall extremes in terms of both dynamics and local-scale thermodynamics over the sub-continent. Moreover, the bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for historic and projected climate for the three scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) using output from 20 General Circulation Models (GCMs) from CMIP6. Hence, the bias-corrected projections suggest that most susceptible places for increasing heavy rainfall extremes likely to be Mumbai, Pune, Panaji in the Western coasts of India (Western Ghats), Itanagar and Shillong in the North-East India, Raipur and Bhopal in the Central India in near- and late-21st century under all scenarios in a warming climate. Thus, the bias-corrected projections from CMIP6-GCMs can be used for hydroclimate impact assessment in Indian region under the changing atmospheric circulation dynamics and warming induced by greenhouse gases.