The statistical downscaling technique based on large-scale numerical forecasting productions is an effective method for fine-scale forecasting.In China,researchers use interpolation methods such as bilinear interpolation and inverse distance interpolation to produce a downscaled forecast.In recent years,the Kalman filter-type self-adapting decaying average downscaling technique has been designed overseas for forecast downscaling,which is better than the MOS method.Based on a daily surface temperature dataset of 752 weather stations for the period 2000 to 2010 in China,a fine-scale prediction test with a low-resolution "forecast field" and fine-scale "analysis field" for daily average temperature,using the self-adapting Kalman Filter-type decaying average statistical downscaling technique,was designed,without the effect of forecasting error in numerical forecasting production.The result of the downscaled prediction was compared with the interpolation method and analyzed for its possibility of application in China.The decaying average technique in this paper filtered the observational data in order and determined the change of the dynamic system constantly.Then,systematic bias (called the "downscaling vector",DV) was estimated.Finally,the prediction outcome was then corrected by the bias.This was a kind of self-adapting bias-estimated method,similar to the Kalman filter and a statistical post-processing method.The DV,defined as the difference between the "forecast field" and "analysis field" at the same time,presents the statistical relationship and systematic bias between the forecast and analysis.The DV that is weight-averaged between the last DV and the forecast error at the same time is updated by the decaying average algorithm.Thus,we can extract error information between the forecast and the observational data to estimate the forecast bias.The result show that: (1)The 1-3 d forecast accuracy rate of the self-adapting Kalman filter-type decaying average statistical downscaling technique was 70%-80%,which is basically satisfactory for professional application.The RMSE of the 1-3 d forecasts was between 1.4 ℃ to 1.7 ℃,and the average value in China was 1.5 ℃.The error increased from Southeast China (RMSE of 1.4 ℃) to Northwest China (RMSE of 1.8 ℃).The forecasting ability decreased with the increase of the forecast limitation.The forecast effect was best in summer and worst in winter. (2)The IDS interpolation method was able to provide the best estimated field,but the decaying average statistical downscaling technique was better than any interpolation method.The critical DV was structured reasonably,able to estimate the interpolation bias of the estimated field well.The RMSE decreased to 50% on average (approximately 1.4 ℃),and the forecast accuracy rate increased by 20%-30% in the 1-3 d forecasts.In particular,the large gap in forecasting ability between West China and East China was reduced,and the accuracy rate increased more than 30% in West China,where there was a large error.In conclusion,the forecasting ability of the method was verified by producing a low-resolution "forecast field" and fine-scale "analysis field" without the influence of forecasting error in the numerical forecasting product.Therefore,the decaying average statistical downscaling technique is feasible for operational fine-scale surface temperature forecasting in China.In future work,we intend to combine numerical forecasting products and reanalysis temperature data to generate a realistic prediction test for demonstrating how the Kalman filter-type self-adapting decaying average downscaling technique performs.