A watershed management program is usually based on the results of watershed modeling. Accurate modeling results are decided by the appropriate parameters and input data. Precipitation is the most important input for watershed modeling. Precipitation characteristics usually exhibit significant spatial variation, even within small watersheds. Therefore, properly describing the spatial variation of precipitation is essential for predicting the water movement in a watershed. This study is concerned with mapping annual precipitation in Jam and Riz watershed of Iran, from sparse point data using Inverse Distance Weighting (IDW) method. The objective in the optimization process is to minimize the estimated error of precipitation. Thus the performance of each interpolation was assessed through examination of mapped estimates of elevation. The results show that the estimated error is usually reduced by this method. Particularly, when optimized exponent in IDW method was selected for digital elevation model which, is secondary variable for the annual average precipitation gradient equation. It was conclude that IDW-3 with the best conditions and lowest mean standard error provides the most accurate estimates of precipitation.