Improving Extended-range Prediction of Midsummer Maximum Temperature over Southern China Using a Dynamical Downscaling Approach

This paper is to improve the prediction ability of maximum temperature (Tmax) in midsummer (July) in the extended-range by using dynamical downscaling method. The prediction skills of Tmax in July over southern China by the Climate Forecast System version 2 (CFSv2) from the National Centers for Environmental Prediction (NCEP) and the high-resolution Weather Research and Forecasting (WRF) model are compared with the Tmax grid data and the reanalysis data from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5), where the WRF model is driven by the CFSv2 multi-sample ensemble hindcast and forecasting experiments. Results show that the WRF model can improve the prediction of Tmax in China, especially in the south of the middle and lower reaches of the Yangtze River, with significant improvement, but there is still cold bias. The simulation biases of daily Tmax in the two models are corrected by using the variance correction method. It is found that the predictions on the average Tmax in annual and extended-range scale by the WRF are remarkably improved after the variance correction. Besides, the predictability sources for the improved prediction in the regional model are analyzed. The WRF with refined parameterization schemes for physical processes is conducive to improve the simulations on the geopotential height fields in the middle and lower troposphere, the surface sensible heat flux and latent heat flux. It suggests that the dynamical downscaling method can obviously improve the temperature prediction in the southern China. Hence, this study has application value for the extended-range prediction of high-temperature.