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.
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