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During recent decades, India experienced more frequent and severe floods due to increasing extreme rainfall events over different Indian River Basins (IRBs). The present study uses Generalized Extreme Value distribution, Expert Team on Climate Change and Detection Indices, and Standardized Precipitation Index to examine the trend in extreme rainfall events over the IRBs using long‐term observed high‐resolution gridded rainfall data (1901‐2019) obtained from India Meteorological Department. The analysis depicts a marked shifting trend in extreme rainfall events from northeastern Indian river basins toward the western Indian river basins during the recent decades of 1981‐2019. The spatial variations in the annual maximum rainfall for the 10‐, 30‐, and 100‐year return levels show statistically significant increasing trends over the IRBs. The observed decadal changes of rainfall during wet and dry conditions showed the shifting and increasing (15%–58.74%) pattern in extreme rainfall events during the last decades of the 20th and current twenty first century over the west‐flowing river basins. This research highlights the significant increasing trend in extreme rainfall events, which may pose a grave risk to agriculture, human life, and infrastructure, predominantly on the vulnerable sections of the society. Key Points: High‐resolution gridded data sets used for the assessment of the rainfall extremes over the Indian River Basins (IRBs)A western shift in a significantly increasing trend of extreme rainfall events was observed over the western part of the IRBs in the last four decadesWest and North‐east flowing river basins were found to be highly flood‐prone regions resulting in vulnerable hazards [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) are routinely applied for regional climate assessments. The RCM simulated rainfall typically overpredict the light rain/drizzle events. To correct the typical errors noted in RCM rainfall simulations, in this study, three bias‐correction methods: linear scaling (SCL), local intensity scaling (LOCI), and empirical quantile mapping (EQM), have been employed. These methods are used to correct monsoon rainfall simulations from 7 RCMs across 14 agroclimatic zones (ACZs) in India from 1970 to 2005. The corrected rainfall data were compared to the observations obtained from India Meteorological Department. The performance of the three methods was assessed using: probability distribution function, consecutive dry day index (CDD), R95 (rainfall distribution at 95th percentile), and spatial correlation. The results vary spatially across the different ACZs. Overall, SCL method is more effective followed by EQM while LOCI was relatively less effective in correcting the errors. Spatial analysis of the rainfall indicates notable improvements over the Western Himalayan Region, which has a complex topography and land use. Error metricsreveal broad improvements across different ACZs, except for Central Plateau Hill Region, East Coast Plain Hill Region, and Southern Plateau Hill Region. The SCL and EQM perform well, and the results are particularly good for simulating wet days (R95), while no distinct variation was found among correction methods to reduce dry bias (CDD). The results provide ACZ and region‐specific utilization of an effective bias correction technique for impact assessment studies in the Indian monsoon region. Key Points: Linear scaling (SCL) and empirical quantile mapping (EQM) performed well, particularly for simulating wet days (R95)Local intensity scaling (LOCI) method was relatively less effective in correcting the errorsNo distinct variation noted among correction methods to reproduce dry bias (CDD) [ABSTRACT FROM AUTHOR]