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Because precipitation is influenced by various atmospheric variables, it is highly nonlinear. Although precipitationas choices of the initial weight, local minima and the number of neurons, etc. In the present paper, we correct simulatedprecipitation by using a multiple linear regression (MLR) method, which is simple and widely used. First of all, Ensemblehindcast is conducted by the PNU/CME Coupled General Circulation Model (CGCM) (Park and Ahn, 2004) for the periodfrom April to August in 1979-2005. MLR is aplied to precipitation simulated by PNU/CME CGCM for the months of June(lead 2), July (lead 3), August (lead 4) and seasonal mean JJA (from June to August) of the Northeast Asian region includingthe Korean Peninsula (110o-145oE, 25-55oN). We build the MLR model using a linear relationship between observedprecipitation and the hindcasted results from the PNU/CME CGCM. The predictor variables selected from CGCM areprecipitation, 500 hPa vertical velocity, 20 hPa divergence, surface air temperature and others. After performing a leave-one-out cross validation, the results are compared with the PNU/CME CGCMs. The results including Heidke skill scores.........Multiple Linear Regression (MLR), Model Output Statistics (MOS), Precipitation over East Asia, CoupledGeneral Circulation Model. . : ... ... .. .... .... .... ... ..... .. ... . ... .. ... .. .