Abstract: Qinling Mountains are a major east-west mountain range in southern Shaanxi Province,China. It extending about 1 500 kilometers across central China from Gansu-Qinghai border in the west to central Henan Province in the east,form a natural dividing line between China's sub-tropical and warm-temperate zones. It also provide a natural boundary between North and South China,and support a huge variety of plant and wildlife, some of which is found nowhere else on the Earth. It plays one of the most important climate boundaries between North and South China. The orographic effects on climate and climatic impacts on hydrological regimes are very important to surrounding areas. Therefore,its precipitation pattern plays an important role on regional and even national vegetation productivity,ecosystem carbon cycle and the ability of water conservation. So far,the studies of optimal interpolation method evaluation are mostly focus on other area,lack of research on complex terrain of Qinling Mountains. In this paper,22 meteorological stations within Qinling Mountains and 19 nearby stations, the annual precipitation of from 2000 to 2015 combined their geographical information and digital terrain model were analyzed with Arcgis spatial analysis package as follows:(1)Direct spatial interpolations of precipitation using five different methods which including Inverse Distance Weighting(IDW),Ordinary Kriging,Universal Kriging,Spline,and Trend Surface Method;(2)Applying the listed interpolation methods again based on the fact that the precipitation increases with increasing elevation;(3)Analyzing the influence of the change of the number of stations on the accuracy of interpolation of precipitation data;(4)Analyzing the influence of interpolation spatial scale on interpolation precision and discussed the optimal interpolation method. In order to find out the optimal method of spatial interpolation of annual precipitation of Qinling Mountains,the mean absolute error (MAE),mean relative error(MRE)and root mean square error(RMSE)of above listed interpolation methods were compared by cross validation based on the interpolation results. The results show as follows:(1)Annual precipitation pattern is irrelevant to topographic but shows a negative correlation with the latitude. Therefore,this paper does not consider multiple regression method;(2)Owing to the terrain complication,the southern slope and the northern slope have their own significant climatic characteristics,currently only considering the precipitation increases with increasing elevation,the interpolation results improved in the west part of south slope of main ridge of Qinling Mountains but not in other areas. The precipitation is not linearly increases with increasing elevation; we need to define the increasing rates on different altitude. The results show that after considering the terrain influence,areas with elevation less than 600 meters or greater than 800 meters have significant improvement. It indicates that only considering the terrain is not sufficient to analyze the precipitation distribution. Due to the complex topography,the difference between north and south slope or the illuminated and shaded slope,further more different increasing rate with increasing elevation need to be taken under consideration for future research. (3)Currently,Qinling Mountains area does not have sufficient precipitation observations,under such circumstances the Spline interpolation with weight of 0.001 provided the ideal results which tested by the meteorological observations;(4)The spatial resolution has minor effect on precipitation interpolation when it between 50 m and 1 000 m. Increasing the number of interpolation stations would not improve the interpolation results. The best result was found when interpolate with 20 stations. The results of the paper would satisfy the researchers who need the spatial information of precipitation in the region,and also provide basic support for ecological model, hydrological model and land surface process researches.

Key words: Qin Mountains, Annual Precipitation, Spatial Interpolation