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A dynamical-statistical post-processing approach is applied to seasonal precipitation forecasts in China during the summer.The data are ensemble-mean seasonal forecasts in summer(June-August)from four atmospheric general circulation models(GCMs)in the second phase of the Canadian Historical Forecasting Project(HFP2)from 1969 to 2001.This dynamical-statistical approach is designed based on the relationship between the 500 geopotential height(Z500)forecast and the observed sea surface temperature(SST)to calibrate the precipitation forecasts.The results show that the post-processing can improve summer precipitation forecasts for many areas in China.Further examination shows that this post-processing approach is very effective in reducing the model-dependent part of the errors,which are associated with GCMs.The possible mechanisms behind the forecast's improvements are investigated.