Abstract: In order to improve objective precipitation forecasting level, non parameter estimate technology is used in research in application and interpretation of numerical prediction products. T213 numerical prediction products from national meteorological center are used as primary data from April to September during 2003 to 2005. By diagnostic analysis and Stepwise Regression, 10—20 factors are selected frommany factors of different levels and various times. The factors from numerical prediction products are well relevant to the rain observation precipitation data. An improved K-nearest neighbor approach (KNN) is used to forecast precipitation and that more than 10 mm at dissimilar area stations from April to September in 2006. In searching K-nearest neighbor process, different types of weather events such as rain free days, drizzle days and moderate rain days, have diverse probability. Then, the different K (K⁺ and K^-) values are computed to match the different weather events. The number of exiting weather event is represented by the value of K⁺. The number of no weather event is represented by the value of K^-. It is reasonable for different weather event to use KNN method. Forecasting and test patterns are selected in turn from history patterns by crossing verification method. Forecasting and test pat terns are replaced by other ones in historical patterns. Until all historical patterns are gone through thoroughly as forecasting and test patterns before an accuracy rate and a summary rate of forecasting are computed. To reduce the rate of miss forecast and to put the main emphasis on accuracy rate and summary rate of forecasting, the values of K⁺ and K^- are continually adjusted. Different accuracy rate and summary rate of forecasting can be computed for different K⁺ and K^- value. The result of tentative forecasting is compared. When both the accuracy rate and summary rate of forecasting are comparatively better, one optimal K is selected from a number of the accuracy rates and the summary rates of forecasting, which are corresponded with optimal K⁺ and K^-. After K⁺ and K_- are chosen, historical patterns are revised. The forecasting and distinguishing value of some stations is computed by comparing the results. To a certain extent, the rate of false forecasting decreases. Based on the forecasting experimentation from April 1st to September 30th in 2006 to forecast 24 hour and 48 hour qualitative prediction of 0 mm and 10 mm precipitation in different area stations, the improved KNN approach obtains a much higher technical score than KNN approach used before. The forecasting results of the improved KNN method are compared with the results of direct model output (DMO) and the result of MOS precipitation prediction. KNN approach gets more technical score than that of DMO and MOS, especially the rate of false forecasting of KNN approach sharply decreases, which is superior to DMO and MOS precipitation forecast, and better than KNN approach used before. It is a useful model for the actual operational forecasting of precipitation.