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| Statistical Downscaling Prediction of Summer Precipitation in Southeastern China |
| LIU Ying,FAN Ke,WANG Hui-Jun |
| 1. Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; 2. Graduate University of the Chinese Academy of Sciences, Beijing 100049, China,1. Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China,1. Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China |
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Abstract A statistical downscaling approach based on multiple-linear-regression (MLR) for the prediction of summer precipitation anomaly in southeastern China was established, which was based on the outputs of seven operational dynamical models of Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction (DEMETER) and observed data. It was found that the anomaly correlation coefficients (ACCs) spatial pattern of June-July-August (JJA) precipitation over southeastern China between the seven models and the observation were increased significantly; especially in the central and the northeastern areas, the ACCs were all larger than 0.42 (above 95% level) and 0.53 (above 99% level). Meanwhile, the root-mean-square errors (RMSE) were reduced in each model along with the multi-model ensemble (MME) for some of the stations in the northeastern area; additionally, the value of RMSE difference between before and after downscaling at some stations were larger than 1 mm d-1. Regionally averaged JJA rainfall anomaly temporal series of the downscaling scheme can capture the main characteristics of observation, while the correlation coefficients (CCs) between the temporal variations of the observation and downscaling results varied from 0.52 to 0.69 with corresponding variations from -0.27 to 0.22 for CCs between the observation and outputs of the models.
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Received: 25 January 2011
Revised: 22 February 2011
Accepted: 22 February 2011
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