To correct the model errors in analogue-dynamical prediction, a new idea of using the analogue prediction of principal components of model errors, instead of analogue prediction of model error directly, is proposed. By decomposing the empirical orthogonal function, the principal components of the model errors are divided into two parts subjectively: predictable and unpredictable. For the predictable part, it is analogically predicted by the scheme of dynamical and optimal configuration of multiple predictors; while for the unpredictable part, it is estimated by average of the system. Based on the National Climate Center (NCC) of China operational seasonal prediction model results for the period 1983-2010 and the US National Weather Service Climate Prediction Center merged analysis of precipitation in the same period, together with the 74 circulation indices of NCC Climate System Diagnostic Division and 40 climate indices of NOAA of US during 1951-2010, the method is implemented in objective and quantitative prediction of monsoon precipitation in Northeast China. The independent sample validation shows that this technique has effectively improved the monsoon precipitation prediction skill during 2005-2010, for which the averaged anomaly correlation coefficients and the system correct of errors are 0.29 and 0.04 respectively. This study demonstrates that the analogue-dynamical approach can enhance the prediction level of NCC operational seasonal forecast model obviously.