| Summary: | Abstract Changes in mean and extreme precipitation characteristics with changing climate may lead to an increase in frequency of hydrological extremes. For studying the impacts of the changing climate on hydrological systems, General Circulation Model (GCM)/Regional Climate Model (RCM) simulated precipitation are used. However, these products should be bias‐corrected before used in hydrological simulations to predict hydrological extremes. Most of the existing bias‐correction techniques suffer from either of two limitations – (a) they only reduce bias in selected precipitation quantile (either mean or extreme values), and/or (b) they exclude zero values from the analysis, even though their presence is significant in daily precipitation. In this study, a stochastic copula‐based bias‐correction method (Maity et al., J. Hydrometeorol., 20, 2019, 595), henceforth RMPH method, is used that corrects the bias in any quantile (mean and/or extreme values) of daily precipitation including zero values. The RMPH method is applied across Indian mainland to correct bias in simulated precipitation from the Coordinated Regional Climate Downscaling Experiment (CORDEX). Due to diverse climatic conditions across India, the quality of bias‐corrected precipitation is studied separately for different meteorologically homogenous regions of the country. Despite non‐uniform distribution of raingauge stations for observed precipitation, the superiority of the bias‐corrected precipitation (from RMPH method) in correcting bias and retaining the seasonal variation across the country is evident when compared with tradition bias‐correction approach like quantile mapping. The new bias‐corrected precipitation dataset developed is particularly suited for hydrological simulations, formulating extreme event mitigation strategies and climate change adaptation strategies.
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