There are still deviations between meteorological products and climate model prediction data and ground observation data, which usually need to be corrected to ensure data reliability; The correction effect of commonly used deviation correction methods is affected by regional characteristics and meteorological elements. For areas covering multiple climate zones, large spatial heterogeneity and comprehensive influence of multiple meteorological elements, the effect of single deviation correction method is not ideal. Therefore, a generalized joint deviation correction method is proposed in this paper. According to the temporal and spatial correlation between rainfall and temperature and their effect on regional hydrological process, The quantitative mapping (QM) method and joint bias correction (JBC) method are coupled for joint correction. The method is applied to the Lancang Mekong River Basin, and the results show that Compared with the (QM) method, the generalized joint deviation correction takes into account the correlation between precipitation and temperature, improves the correction effect of precipitation and temperature extreme values, and the Nash coefficient increases significantly, especially in May and June, the Nash coefficient increases by more than 0.5; compared with joint bias correction Compared with (JBC) method, this method considers the dynamic relationship between meteorology and hydrology, reduces the deviation of precipitation and temperature frequency distribution and mean value, and makes the modified data distribution closer to the measured data distribution; when the modified meteorological data is used to drive the distributed hydrological model, the accuracy of runoff simulation is improved by 91.2%; on this basis, the modified meteorological data of this method is applied to the future meteorology and hydrology of the basin forecast. The generalized joint deviation correction method realizes the complementary advantages between traditional methods, extends the scope of application, improves the accuracy of meteorological data correction, and provides a reliable basis for future runoff prediction and water resources planning and management of subsequent basins.