降水产品的精度受到多种因素的影响，相对于地面站点观测降水表现出不同程度的偏差，需在实际应用前进行偏差校正并评估其适用性。为探寻适用于不同降水产品在长江流域不同区域内的偏差校正方法，评估CMFD、MSWEP和ERA5-Land这3种降水产品在长江流域内3个气候、地形和面积有较大差异的子流域内的适用性，并在此基础上对比使用3类7种方法对日尺度降水值进行偏差校正。结果表明：校正后的降水产品的适用性得到了不同程度的提升，而局部强度缩放与三伽玛分布结合校正法在3种降水产品和3个子流域的校正效果均为最优。校正方法的校正幅度与降水产品在研究区域的适用性存在反相关关系，产品的适用性越差，精度评价指标的改进幅度越明显。研究结果可为后期不同降水产品在长江流域不同子流域的准确应用提供可参考的校正方法。

As a key link in the water cycle, the uneven and unstable spatial and temporal distribution of precipitation directly causes natural disasters such as floods and droughts. Highly accurate precipitation data not only helps the research of hydrology, meteorology, and agriculture, but also can effectively be used to mitigate natural disasters such as floods. Although station observation is the most direct and accurate way to obtain precipitation, the distribution of ground stations is not uniform, which often does not reflect the spatial distribution of precipitation well. Precipitation products with high resolution, continuous spatial and temporal distribution, and easier access make up for the shortcomings of ground stations to a certain extent and are widely used in basin hydrological simulation and extreme precipitation prediction. However, their accuracies are affected by the nature of cloud cover, the performance of satellite sensors, and the inversion algorithms, which often show different degrees of bias, and need to be evaluated for applicability to verify their reliability before use. It has been shown that appropriate bias correction for less-than-optimal precipitation products can effectively improve their applicability.The bias correction of precipitation products are compared using seven methods in three categories to explore the bias correction methods applicable to different precipitation products and to evaluate the applicability of three precipitation products such as CMFD, MSWEP, and ERA5-Land based on the day-by-day precipitation observation data from surface meteorological stations from 1979 to 2018 in three sub-basins of the Yangtze River basin. Three types of precipitation products are compared with seven methods for bias correction, and nine evaluation indexes describing the detection capability, error size, degree of fit, and comprehensive evaluation are used to analyze and evaluate the applicability and correction effect to provide scientific and reasonable bias correction methods for the accurate application of precipitation products in different regions of the Yangtze River basin.The results show that the applicability of the corrected precipitation products was improved to different degrees, and the correction method combining local intensity scaling and triple gamma distribution was optimal for all three precipitation products and three sub-basins. There was no obvious relationship between the correction method and the type of precipitation products and the geographic location of the study area. There is an inverse correlation between the magnitude of correction of the correction methods and the applicability of the precipitation products in the study area, and the worse the applicability of the products, the more obvious the improvement of the accuracy evaluation indexes.The research can provide a referable calibration method for the accurate application of different precipitation products in different sub-basins of the Yangtze River basin at a later stage. A new correction method, local intensity scaling combined with three gamma distribution is constructed based on the segmented three-gamma distribution method by introducing the quantile optimization module and combining it with the local intensity scaling method, and it is applied to the correction of precipitation products. Compared with other bias correction methods, the combined local intensity scaling and triple gamma distribution method shows the best correction effect for the three precipitation products in three sub-basins, which is not only capable of correction, but also applicable, and can perform well and stably for the precipitation products with different characteristics and in regions with large differences in climate, topography and area.