Probabilistic seasonal prediction of summer rainfall over East China based on multi-model ensemble schemes

The skill of probability density function (PDF) prediction of summer rainfall over East China is evaluated by skill of optimal ensemble schemes, based on the precipitation data from five coupled atmosphere-ocean general circulation models in ENSEMBLES. The optimal ensemble scheme in each region is the scheme with the highest skill in the four commonly-used ones: the equally-weighted ensemble (EE), EE for calibrated model-simulations (Cali-EE), the ensemble scheme based on multiple linear regression analysis (MLR), and the Bayesian ensemble scheme (Bayes). Results show that the optimal ensemble schemes are the Bayes in the southern part of East China, the Cali-EE in the Yangtze River Valley, the Yangtze-Huaihe River Basin and the central part of northern China, and the MLR in the eastern part of northern China. Their PDF predictions are well calibrated, and are sharper than or have approximately equal interval-width to the climatology prediction. In all regions, these optimal ensemble schemes outperform the climatology prediction, which indicate that current commonly-used multi-model ensemble schemes have been able to produce skillful PDF prediction of summer rainfall over East China, even though more information from other model variables is not derived.