Extreme temperatures are key drivers controlling both biotic and abiotic processes, and may be strongly modified by topography and land cover. We modelled mean and extreme temperatures in northern Fennoscandia by combining digital elevation and land cover data with climate observations from northern Finland, Norway and Sweden. Multivariate partitioning technique was utilized to investigate the relative importance of environmental variables for the variation of the three temperature parameters: mean annual absolute minima and maxima, and mean annual temperature. Generalized additive modeling showed good performance, explaining 84–95 % of the temperature variation. The inclusion of remotely sensed variables improved significantly the modelling of thermal extremes in this system. The water cover variables and topography were the most important drivers of minimum temperatures, whereas elevation was the most important factor controlling maximum temperatures. The spatial variability of mean temperatures was clearly driven by geographical location and the effects of topography. Partitioning technique gave novel insights into temperature-environment relationship at the meso-scale and thus proved to be useful tool for the study of the extreme temperatures in the high-latitude setting.