Prediction of Monthly Precipitation and Number of Extreme Precipitation Days with Statistical Downscaling Methods Based on the Monthly Dynamical Climate Model

The prediction of precipitation especially extreme precipitation is important but difficult. Dynamical climate models play important roles in the climate prediction and show good skills in large-scale circulation prediction. However, its prediction skill of daily precipitation is limited on regional or smaller spatial scale. So dynamical or statistical downscaling is developed to provide prediction with high resolution. Statistical downscaling can make full use of the large-scale circulation information with high skill of global climate model, and simulate everyday climate variables on the regional or point scale. It has become a popular method in climate prediction and climate change research.Dynamical Extension Regional Forecast Model (DERF) by National Climate Center, CMA has been used in the climate prediction for nearly ten years. Like other global climate models, it has good skills in predicting circulation fields such as height, wind, and sea level pressure. Optimum subsets regression (OSR) is used to predict precipitation anomaly at 133 stations in China for 6 periods (1—10 days, 11—20 days, 21—30 days, 31—40 days, 1—30 days, 11—40 days) using geopotential height, zonal wind, meridional wind and sea level pressure as predictors by DERF. The OSR models are verified with cross validation method using data from 1982 to 2006. Five operational sores (Ratc, CLTc, P, ACC and TS) are compared with the results directly forecasted by DERF. The results show that OSR can improve prediction skill to different extents, especially for 11—40 days. Then two statistical downscaling methods are used to predict number of extreme precipitation days. One is predicting directly as predictant with OSR method using large circulations from DERF as predictors (named as 1-step method), which is similar to precipitation anomaly prediction. The other one is to compute the day number using simulation results of weather generator (WG) under the condition of precipitation anomaly predicted by OSR downscaling (named as 2-step method). Random prediction is compared with the two methods. Crossing verification from 1982 to 2006 show that the predict skill of the two statistical methods is better than that of random prediction. The skill of 2-step method is better than 1-step method to predict number of extreme precipitation days in winter, but worse in summer. It can be concluded that the methods of OSR and combination of OSR and WG have high skill to predict precipitation and number of extreme precipitation days. The prediction information can provide important information for short-range climatic prediction.