Physical–empirical prediction model for the dominant mode of extreme high temperature events in eastern China during summer

Abstract

The dominant mode of extreme high temperature events in eastern China during summer shows a dipole pattern with opposite anomalies over southern and northeastern China, which explains 25% of the variance. We document the limited prediction skill of the National Centers for Environmental Prediction Climate Forecast System, version 2 (CFSv2) for the dominant mode of these events. Based on the mechanisms related to the dominant mode, a physical–empirical model was established that improved the prediction of extreme high temperature events in eastern China, which will help in disaster prediction and mitigation. The physical connection between the dominant mode and the summer sea surface temperature (SST) over the western tropical and subtropical Pacific Ocean was predicted well by the CFSv2 hindcasts, and thus the areal mean CFSv2-hindcast SSTs over the western tropical and subtropical Pacific Ocean served as a predictor in the physical–empirical model. In the observations, the soil moisture over southern China in the preceding December (soil_Dec) combined the signal of the air–sea interactions over the tropical and northern extratropical Pacific Ocean, which induced anomalous SSTs in the Indian and western tropical Pacific oceans in the following summer and contributed to the dominant mode. Soil_Dec was therefore closely correlated with the dominant mode and was used as a predictor. The results of the one-year-leave cross-validation and independent hindcast showed that the time series of the dominant mode was better predicted by the physical–empirical model than by the CFSv2 hindcasts, with an improved correlation coefficient from insignificant to about 0.8, a root-mean-square error reduced by about 50% and an increased rate of same signs. The physical–empirical model showed advantages in the prediction of the dominant mode of summer extreme high temperature events over eastern China, which may be used in the prediction of other climate variables.