Abstract: Using the 20-year hindcast results by the second generation of the Institute of Atmospheric Physics, Chinese Academy of Sciences（IAP） Dynamical Climate Prediction system （IAP-DCP II）, the bias correction methods based on the Empirical Orthogonal Function (EOF) and the Singular Value Decomposition (SVD) have been proposed and validated for the prediction of precipitation anomalies, furthermore, the efficiency of these two correction methods for the different seasons has been investigated, together with their applicability to the different predictive variables. The verification results show that, both of the methods can significantly improve the prediction skill of precipitation anomalies over China by the IAP-DCPII with about 80% predictions improved obviously and the SVD-based method showing a higher stability compared with the another method. Generally, the EOF-based method is better in summer and winter, with the anomaly correction coefficients (ACC) between the hindcast and the observation improved from -0.07 to 0.25 in summer, and from 0.16 to 0.44 in winter, respectively, while the SVD-based method performs better in spring and autumn season, with the ACCs improved from -0.09 to 0.36 in spring, and from -0.07 to 0.30 in autumn, respectively. In addition, for the sake of real-time application, the summer surface air temperature and the 500 hPa geopotential height are also used to evaluate the efficiency of the two methods. However, for the IAP-DCP II, it is proved that both of the bias correction methods are not applicable for the prediction of the surface air temperature and the 500 hPa geopotential height. The some prime reasons are shown based on the results of two ideal experiments with and without cross-validation respectively.