Evaluation Of The Prediction Skills Of Precipitation In The Pre-flood Season In South China Based On NUIST CFS1.

Current dynamical models have great difficulties in providing reliable seasonal forecasts of regional/local rainfall in South China.This study evaluated seasonal forecast skill of precipitation in the first rainy season（i.e.April-June）over South China from 1982 to 2020based on the real-time global Climate Forecast System of Nanjing University of Information Science and Technology（NUIST CFS1.0,previously known as SINTEX-F）.（1）The overall potential predictability of precipitation in the first rainy season over South China is relatively low.The prediction skills of NUIST CFS1.0 and nine international models from the COPERNICUS Climate Change Service（C3S）for precipitation climatology and interannual anomalies performed mediocrely,which may be due to the fact that precipitation in the first rainy season is driven by complex mechanisms and strong atmospheric internal variations,indicating large uncertainty in the forecasts.The results show that NUIST CFS1.0performs better than the average of the nine international models from C3S in deterministic prediction skills.In addition,the deterministic forecasts from the three different initial months show a similar level of the skill,while the prediction skills initiated from Mar.1^st and Jan.1^stare slightly better than that initiated on Feb.1^st.The prediction ability of NUIST CFS1.0 for wet and dry years performs better than that for normal years.（2）The forecast skills of NUIST CFS1.0 for the interannual precipitation anomaly originate from two aspects.First,it could capture the anomalous Philippines anticyclone,which transports moisture and heat northward to South China,favoring more precipitation in South China during the first rainy season,and vice versa.Moreover,by examining the correlations between sea surface temperature（SST）and the first rainy season precipitation and the Philippines anticyclone,we found that the model could reasonably capture the SST-associated precipitation and circulation anomalies,which partly explains the model’s predictability of the first rainy season precipitation.（3）A dynamical downscaling model with 30-km resolution forced by the large-scale circulations of the NUIST CFS1.0 predictions could improve the forecasts of the climatological states and extreme precipitation events.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.（4）In addition,there are interdecadal changes in the prediction of interannual precipitation anomalies based on NUIST CFS1.0 during the first rainy season over South China,mainly reflected in the significant interdecadal changes in the correlations between the two key regions of the tropical Indian Ocean and the tropical eastern Pacific Ocean and the precipitation in the first rainy season over South China,which cannot be well captured by NUIST CFS1.0.Therefore,further research in the physical mechanism of interdecadal changes of SST and its relation with the first rainy season precipitation is of great significance for improving the prediction skills of NUIST CFS1.0.