Understanding of climate spatial distribution depending on topography is the key to environmental and resource management. However, there is a limit to factor climate into ecological study because most climatic data are obtained from a sparse, irregularly distributed meteorological network with unchecked data errors. Interpolation technique for estimating climate at any location from data points is demanded. This paper demonstrated the generation of gridded climate data in regular space by coupling thin plate smoothing spline surfaces of monthly mean minimum temperature, mean maximum temperature and precipitation to underlying 1 km resolution digital elevation model (DEM) for China. The thin plate smoothing spline involves topographically dependence of climate with linear sub-model for accurate interpolation. It provides a series of diagnostic procedures for data error detection and correction. The predictive errors of temperature are within 0.6℃ and for precipitation in range of 6%-12%. The data of other climate variables with biological meanings can be derived directly from interpolated surfaces and grids. The developed regular grid of climate will be used primarily as unchanged climate condition for study of climate change. There are various potential applications in spatial prediction of flora species, identifying priority areas of biodiversity and development of ecoregions.