Abstract: Aimed at requirement for daily weather data with high resolution which is used to scaling up the crop growth simulation models, the spatial interpolation methods of daily weather data from April to October in Northeast China are studied using ordinary Kriging and other two relatively simple methods: inverse distance weighting (IDW) technique with weighting of longitude and latitude, and gradient of height plus inverse-distance-weighting (GIDW) technique. The statistics analysis for interpolated values and cross verification for estimated values are made. The results indicate that for temperature, the precision of the evaluated value is the highest, and the smoothness is moderate, in addition, the space distributing trend of the interpolated result is more close to the actual data using GIDW method. For precipitation, the precision of the evaluated value with IDW method is higher than that with the Kriging method, and the smooth level of the interpolated result is smaller, and it is more fit to the interpolation of the daily precipitaion. The cause of the higher precision of the vealuated values with GIDW and IDW methods is that the obvious season changes in longitude, latitude and sea-level-elevation gradient of the daily maximum temperature, minimum temperature and precipitation are considered, the weights in longitude and latitude for meteorological factors are determined, and the meteorological factors are also corrected by daily sealevel-elevation gradient.