Abstract: The utilization of statistical post-processed numerical precipitation forecasts is a significant approach to extend the effective forecast period of hydrological forecasting. Existed statistical post-processing methods struggle to simultaneously correct dichotomous and quantitative errors, and their impact on the effective forecast lead time for precipitation forecasting is frequently overlooked. In this study, we introduce a novel post-processing scheme called EQM-BMGD, which combines the Empirical Quantile Mapping model (EQM) and the Bernoulli-meta-Gaussian Distribution (BMGD). Additionally, we establish a comprehensive accuracy metric for evaluating the effective forecast period. Using the Han River Basin as a case study, comparative outcomes showed that EQM-BMGD integrated the strengths of the two individual methods, achieving precipitation forecasts with superior accuracy. The forecast accuracy (O_P) and mean absolute error (E_MA) of the post-processed average-basin forecasts increased by more than 10%, the O_P of the forecast period 222—228 h was still close to 0.7, and E_MA was less than 0.7 mm/(6 h), and the EFPs were extended by 18—66 h. On a grid scale, the gains of O_P and E_MA for the 96—102 h forecast period exceeded 10% and 20% respectively for all grids. Except for a few grids in the southwest, the O_P surpassed 0.8 while the E_MA remained below 1.0 mm/(6 h). In addition, the EFPs of the grids in the northern part were lengthened by 18—54 h. It is demonstrated that the EQM-BMGD can effectively correct both categorical and quantitative errors, thereby enriching the available methodologies for statistical post-processing of numerical precipitation forecasts.