Abstract: Based on ECMWF fine grid point precipitation forecast data and the three source fusion precipitation products by China Meteorological Science Data Information Network, we analyzed and compared the disadvantages and applicability of data conversion from grid points to station and grid point to more high resolution grid using different methods, including nearest neighbor method, inverse distance weighting, least square method and bilinear interpolation method. On the basis of the relative accuracy and fineness of the model forecast, the results show that: 1) The nearest neighbor method had the highest forecast skills on the data conversion from grid points to station, the second is the inverse distance weighting method; 2) On the precipitation field parsing to more high resolution grid, four different methods and the original fields have some differences, but the least square method produces the most serious space pattern distortion; 3) Average TS score of the inverse distance weighting method is the best one on parsing rough network grid to fine network grid, while the nearest neighbor method has a better ETS score.