Monitoring of variables like temperature, precipitation, and air quality is performed to determine their current situation, exhibit the presence of trends and occurrence of outliers. These variables are measured at specific locations and to obtain a full estimation map, we need to predict values at unknown locations. This study focuses on making a minimum air temperature map using copula interpolation with the spline family. Minimum temperature observations for January 2017, all months of 2017, and seasonal averages are analysed over the Euphrates Basin in Turkey. The minimum temperature observations have a high variability due to the varying topography of the area, ranging between -2 C and +14 C in whole of 2017. The interpolation methods incorporated the above mean sea level elevation map and remotely-sensed land surface temperature. We evaluated the accuracy of the predictions using ten-fold cross-validation and compare copula interpolation with External Drift Kriging (KED). The study shows that copulas provided more accurate predictions than KED for most of the months, and for the summer and autumn seasons, whereas KED produced high accurate predictions for January 2017. Results of this study indicate that copulas are able to detect variation in minimum temperatures accurately in areas where topography and observation values are highly variable. Further work will focus on copula-based space–time interpolation techniques for the minimum temperature mapping.