Abstract: The characteristics of the multi-time scale variation of rainfall over Jiangnan during Meiyu season have been analyzed by using the national standard data of Meiyu from China Meteorological Administration. On this basis, we developed a hybrid statistical downscaling prediction model (HSDPM) based on a time-scale decomposition approach, which effectively improved the forecasting ability of rainfall over Jiangnan during Meiyu season (JNMYR), by observing well-known sea surface temperature (SST) indices and outputs from Climate Forecast System version 2 (CFSv2) hindcasts and predictions. The results showed that: (1) There are significant interannual and interdecadal variability of JNMYR, and the standard deviations of the two are 120.1mm and 100.3mm, respectively. (2) We found that the interannual variability of the JNMYR is closely related to the observed tropical Pacific SST anomalies (or ENSO) in the preceding winter and the sea level pressure in June–July over the Northwest Pacific by CFSv2 predicted in May. On the other hand, the interdecadal variability of the JNMYR is linked to the area of the Western Hemisphere Warm Pool (WHWP) in prewinter according to the observations, the sea level pressure over the Northwest Pacific and the 200hPa zonal wind in the tropical Indian Ocean in June–July by CFSv2 predicted in May. (3) On this basis, both the interannual and interdecadal components of the JNMYR are effectively predicted using the corresponding predictors via multiple regression; thus, the HSDPM is ultimately established by combining the above two components. Compared with the original CFSv2 model, the HSDPM model achieves a considerable improvement in performance in predicting the JNMYR. Specifically, the temporal correlation coefficient and anomaly sign consistency rate between the observed and HSDPM-predicted JNMYR in the independent validation period (2014–2023) are 0.76 and 90.0%, respectively, which are significantly greater than the above two forecasting index values of 0.12 and 50.0%, respectively, obtained from the original CFSv2 predictions. The application of the HSDPM may be beneficial for drought and flood prevention and mitigation in Jiangnan during Meiyu season.