Seasonal Forecast of Early Summer Rainfall at Stations in South China Using a Statistical Downscaling Model

Current dynamic models are not able to provide reliable seasonal forecasts of regional/local rainfall. This study aims to improve the seasonal forecast of early summer rainfall at stations in South China through statistical downscaling. A statistical downscaling model was built with the canonical correlation analysis method using 850-hPa zonal wind and relative humidity from the ERA-Interim reanalysis data. An anomalous southwesterly wind that carries sufficient water vapor encounters an anomalous northeasterly wind from the Yangtze River, resulting in a wet anomaly over all of South China. This model provided good agreement with observations in both the training and independent test periods. In an independent test, the average temporal correlation coefficient (TCC) at 14 stations was 0.52, and the average root-mean-square error was 21%. Then, the statistical downscaling model was applied to the Climate Forecast System, version 2 (CFSv2), outputs to produce seasonal forecasts of rainfall for 1982-2018. A statistical downscaling model improved CFSv2 forecasts of station rainfall in South China with the average TCC increasing from 0.14 to 0.31. Forecasts of South China regionally averaged rainfall were also improved with the TCC increasing from 0.11 to 0.53. The dependence of forecast skill for regional average rainfall on ENSO events was examined. Forecast error was reduced, but not statistically significant, when it followed an El Nino event in both CFSv2 and the downscaling model. While when it followed an EP-type El Nino, the significantly reduced forecast error (at the 0.1 level) could be seen in the downscaling model and CFSv2.