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The basis of climatic data is measurements taken at a predetermined chronological order at air monitoring stations, so all measured values ​​of climatic elements can only be attributed to the point of measurement. Therefore, different interpolation methods can help a lot in estimating climate data in different places. The study area in this study is northeastern Iran, including the provinces of Khorasan Razavi and North Khorasan, and monthly summer temperature data were used for 21 synoptic and evaporative stations in the northeast belonging to the Meteorological Organization and the Ministry of Energy with appropriate distribution. The statistical period of 21 years (1997-1997) was considered as a common statistical period for all stations. Then, in order to compare the interpolation methods, several different methods, including ordinary kriging, spline, inverse squares and Thyssen were used by ARCGIS software. Comparing the deviations of the estimates from the measured data was evaluated by cross-validation. Then, in order to check the hypothesis of normality of the calculated errors in each interpolation method, the test thigh test was used and finally, to evaluate the best interpolation method, AHP method and Expert Choice software were used. The results showed that based on the root mean square error (RMSE) criterion, Thyssen, Kriging, inverse distance and spline methods were located, respectively. Based on the absolute maximum error criterion (MAE), the kriging method estimates the summer temperature better than other methods. According to the MBE standard, the kriging method is better than other methods and can be used for temperature interpolation. According to the set coefficients, the calculated compatibility rate is 0.03, which indicates the high accuracy of the selection of weights.