Interpolation of meteorological data from SASSCAL-WeatherNet (Muche et al., 2018) observational land surface stations provides additional information for a data-sparse region. As an application example, different spatial interpolation methods for maximum and minimum temperature have been tested to produce a gridded dataset for the SASSCAL region. We tested the interpolation for the time period of September 2014 to August 2016, as this period had the highest availability of observational temperature data. The best interpolation was achieved by combining multiple linear regression (elevation, a continentality index, and latitude as predictors) with three-dimensional inverse distance weighting (Eiselt et al., 2017).