Determining the potentials of the renewable energy sources provides realistic assumptions on useful utilization of the energy. Wind speed and solar radiation are the main meteorological data used in order to estimate renewable energy potential. Stated data is considered as point source data since it is collected at meteorological stations. However, meteorological data can only be significant when it is represented by surfaces. Spatial interpolation methods help to convert point source data into raster surfaces by estimating the missing values for the areas where data is not collected. Besides the purpose, the total number of data points, their location, and their distribution within the study area affect the accuracy of interpolation. This study aims to determine optimum spatial interpolation method for mapping meteorological data in northern part of Turkey. In this context, inverse distance weighted (IDW), kriging, radial basis, and natural neighbor interpolation methods were chosen to interpolate wind speed and solar radiation measurements in selected study area. The cross-validation technique was used to determine most efficient interpolation method. Additionally, accuracy of each interpolation method were compared by calculating the root-mean-square errors (RMSE). The results prove that the number of control points affects the accuracy of the interpolation. The second degree IDW (IDW2) interpolation method performs the best among the others. Thus, IDW2 was used for mapping meteorological data in northern Turkey.