Accurate and reliable precipitation forecasts are crucial for effective water resource management, disaster risk reduction, and agricultural planning. This paper proposes a Bayesian Modified Bridging (BMB) model, an improved statistical post-processing model based on the Bayesian Joint Probability (BJP) approach, to correct and bridge ensemble precipitation forecasts. The study evaluates the SEAS5 seasonal ensemble precipitation forecasts from ECMWF, covering precipitation across China. By comparing the correction effects of the BJP and BMB models, the aim is to provide more accurate and reliable precipitation forecasts. The BMB model incorporates both the mean and variance of the raw ensemble forecasts, enhancing the correction process's precision and reliability. Comprehensive evaluations using Continuous Ranked Probability Skill Score (CRPSS) and Probability Integral Transform Skill Score (PITSS) show that the BMB model significantly improves the accuracy and reliability of forecasts compared to the BJP model, with average CRPSS and PITSS improvements of 13.9% and 8.8%, respectively, for lead times of 0–5 months. By analyzing precipitation data across 3,781 grid points nationwide, the study finds that the BMB model consistently outperforms the BJP model in all forecast periods and months. These findings highlight the superior correction capabilities and robustness of the BMB model, providing a scientific basis for its widespread application in future research and operational forecasting. This study not only offers new methodologies for the meteorological and hydrological fields but also provides critical technical support for decision-makers in water resource management and disaster prevention.