Prediction And Dynamic Downscaling Of Summer Precipitation In The Middle And Lower Reaches Of The Yangtze River Based On NUIST-CFS1.0

Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River(MLYR)is of urgent demand for the local economic and societal development.This study assessed the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology(NUIST-CFS1.0,previously SINTEX-F)and Weather Forecast and Research(WRF)dynamical downscale.The main results are given as below.The NUIST-CFS1.0 can provide moderate skill in predicting the interannual variations of the MLYR rainbands at 3-month lead.In addition,the nine-member ensemble mean can realistically reproduce the links between the MLYR precipitation and tropical sea surface temperature(SST)anomalies.However,the individual members show great discrepancies,indicating large uncertainty in the forecasts.Compared with seven state-of-the-art global climate models from the Copernicus Climate Change Service(C3S),the NUIST-CFS1.0 could provide world-class skills in predicting the MLYR summer precipitation and western North Pacific anticyclone.From the perspective of probabilistic prediction,the NUIST-CFS1.0displays useful skills in predicting the precipitation anomalies over the MLYR in summer especial for dry events with higher reliability and resolution than the predictions of wet events.The reliability curves and frequency histograms of the forecast probabilities for the dry and wet anomalies over the MLYR indicate that the NUIST-CFS1.0 forecasts are over-confident.The WRF downscaling hindcast experiments with a finer resolution of 30 km,which are forced by the large-scale information of the NUIST-CFS1.0 predictions with a spectral nudging method,display improved skills in predicting the spatial distribution of the summer precipitation anomalies to some extent.Further analysis of the predictions of extreme precipitation anomalies from the global and regional models shows that the performance of the downscaling predictions is highly dependent on the global model's forecast skill,suggesting that further improvements on both the global and regional climate models are necessary.