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This study focuses on how the representation of daily precipitation probability distributions may affect the application of equidistant quantile matching (EDCDFm). Five representations, that is, two parametric distributions and three nonparametric approaches, are selected. The parametric distributions include Gamma and Weibull while the nonparametric approaches include empirical cumulative distribution function (ECDF) and kernel density estimation method (KDE) as well as an improved KDE named diffusion‐based kernel density estimation (DKDE). All five methods were applied to correct the daily precipitation of 11 stations over the Daqing River Basin, North China in 1981–2015. The results demonstrated that DKDE is closer to ECDF than the other three methods in the goodness‐of‐fit evaluation. Furthermore, nonparametric methods present advantages over parametric methods; especially, DKDE and ECDF are skilful equally and both of them display impressive comprehensive performance than other methods by multi‐index. These findings suggest that representing the precipitation probability distributions accurately is beneficial for the bias correction and selecting one or more suitable indexes is of great significance to the verification of EDCDFm. This study can provide guidance for future water resources management of the Daqing River Basin. [ABSTRACT FROM AUTHOR]

Understanding dynamic future changes in precipitation can provide prior information for nonpoint source pollution simulations under global warming. However, the evolution of the dependence structure and the unevenness characteristics of precipitation are rarely considered. This study applied a two-stage bias correction to daily precipitation and max/min temperature data in the Daqing River Basin (DQRB) with the HadGEM3-RA climate model. Validated from 1981 to 2015, future scenarios under two emission paths covering 2031–2065 and 2066–2100 were projected to assess variations in both the amount and unevenness of precipitation. The results suggested that, overall, the two-stage bias correction could reproduce the marginal distributions of variables and the evolution process of the dependence structure. In the future, the amount of precipitation in the plains is expected to increase more than that in the mountains, while precipitation unevenness, as measured by relative entropy, shows a slight increase in the mountains and a decrease in the plains, with enhanced seasonality. Conditioned on rising temperatures, high-/low-intensity precipitation tends to intensify/weaken precipitation unevenness. Additionally, the potential application of the bias correction method used herein and the possible impacts of uneven precipitation on nonpoint source pollution are given for further analyses. This study can provide useful information for future nonpoint source pollution simulations in the DQRB. [ABSTRACT FROM AUTHOR]