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Satellite precipitation estimates  (SPE) provide useful  input  for hydrological modeling. However, hydrological modeling is frequently hindered by large bias and errors in SPE, inducing the  necessity  for  bias  corrections.  Traditional  distribution  mapping  bias  correction  of  daily precipitation  commonly  uses  Bernoulli  and  gamma  distributions  to  separately  model  the probability  and  intensities  of  precipitation  and  is  insufficient  towards  extremes.  This  study developed an improved distribution mapping bias correction method, which established a censored shifted mixture distribution (CSMD) as a transfer function when mapping raw precipitation to the reference data. CSMD coupled the censored shifted statistical distribution to jointly model both the precipitation occurrence probability and intensity with a mixture of gamma and generalized Pareto distributions to enhance extreme‐value modeling. The CSMD approach was applied to correct the up‐to‐date SPE of Integrated Multi‐satelliE Retrievals for Global Precipitation Measurement (GPM) with near‐real‐time “Early” run (IMERG‐E) over the Yangtze River basin. To verify the hydrological response of bias‐corrected  IMERG‐E,  the  streamflow of  the Wujiang River basin was  simulated using Ge ́nie Rural with 6 parameters (GR6J) and Coupled Routing Excess Storage (CREST) models. The  results  showed  that  the  bias  correction  using  both  BerGam  (traditional  bias  correction combining  Bernoulli  with  gamma  distributions)  and  the  improved  CSMD  could  reduce  the systematic errors of  IMERG‐E. Furthermore, CSMD outperformed BerGam  in  correcting overall precipitation (with the median of mean absolute errors of 2.46 mm versus 2.81 mm for CSMD and BerGam respectively, and the median of modified Nash–Sutcliffe efficiency of 0.39 versus 0.29) and especially  in  extreme  values  for  uniform  format  and  particular  attention  paid  to  extremes.  In addition,  the hydrological  effect  that CSMD  correction  exerted on  IMERG‐E, driving GR6J  and CREST rainfall‐runoff modeling, outperformed that of the BerGam correction. This study provides a  promising  integrated  distribution  mapping  framework  to  correct  the  biased  daily  SPE, contributing to more reliable hydrological forecasts by informing accurate precipitation forcing.

A Censored Shifted Mixture Distribution Mapping Method to Correct the Bias of Daily IMERG Satellite Precipitation Estimates