Heat resource is one of the most important agricultural climate resources. It will be very helpful for agricultural climate zoning and guiding agriculture development to use scientific methods to make refinement calculation in how the heat resource will change with different topography elements such as position, direction or altitude. In this paper, the data from 651 basic meteorological observation stations were used to calculate the distribution of the national heat resource, which contains the average temperature data and the highest temperature data of January and July between 1998 and 2007. The IDW (inverse distance weighted), GIDW (gradient inverse distance weighted), spline function, kriging and trend surface analysis methods were used in calculating the heat resources, and the different results were compared. It has shown that the minimum error of GIDW is below 5% while using GIDW methods in calculating the heat resource distribution that can reflect the influence from terrain and altitude. Crossvalidation was used to analyze the error and distribution. Based on GIS software platform, the GIDW was used to draw a lot of distribution maps by use of the 1971—2000 heat resource data from 2346 stations all around China. Average temperature distribution maps in tendays, month, season, year scale and the distribution of the beginning date, the ending date, the days between beginning and ending and accumulated temperature of stably passing through 0, 5, 10, 12 and 15 ℃ at spatial resolution of 0.01°.