The Belt and Road initiative was a globalization cooperation initiative put forward by China to strengthen the opening-up in the new era. With the development of globalization, it is of great significance to optimize the allocation of resources and environment. As an important reference dataset and input factor, the result of temperature interpolation is the basis for optimal allocation of regional resources and environment in large scale study area. Here, taking the Belt and Road (BR) regions as the study area, the monthly and annual mean temperature data in 2679 meteorological stations from 1980 to 2017 were interpolated based on Geographic Information Technology (GIS), using Inverse Distance Squared (IDS), CoKriging (CK), Regression-IDS (RIDS) and Regression-CK (RCK) interpolation methods. The 10 km map of spatial interpolation were generated using aforementioned four methods. The results showed: (1) In the BR regions, the geographical distribution of temperature were better displayed by IDS, CK, RIDS and RCK. The Mean Square Root Error (RMSE) of monthly mean temperature were 1.93~2.43 ℃, 1.78~2.14 ℃, 1.31~2.23 ℃ and 1.23~1.92 ℃, IDS, CK, RIDS and RCK, respectively. And the RMSE of annual mean temperature were 1.94 ℃, 1.83 ℃, 1.37 ℃ and 1.27 ℃, IDS, CK, RIDS and RCK, respectively. (2) The accuracy of CK interpolation with covariates was better than that of IDS, and the peak values produced by IDS were corrected. (3) After considering the impact of terrain, the accuracy of interpolation in temperature based on Residual correction were improved by 29.4% and 30.6%, RIDS compared to IDS and RCK compared to CK, respectively. In summary, The Regression-CK performed better than other three methods in this study area and it can be considered as temperature and climate data interpolation methods in the BR regions.