Gerlitz, LarsBechtel, BenjaminZakšek, KlemenKawohl, TobiasBöhner, JürgenPage, BerndFleischer, Andreas G.Göbel, JohannesWohlgemuth, Volker2019-09-162019-09-162013https://dl.gi.de/handle/20.500.12116/25914Many climate change impact studies require surface and near surface temperature data with high spatial and temporal resolution. The resolution of state of the arte climate models and remote sensing data is often by far to coarse to represent the meso- and microscale distinctions of temperatures. This is particularly the case for regions with a huge variability of topoclimates, such as mountainous or urban areas. Statistical downscaling techniques are promising methods to refine gridded temperature data with limited spatial resolution, particularly due to their low demand for computer capacity. This paper presents two downscaling approaches one for climate model output and one for remote sensing data. Both are methodically based on the FOSS-GIS platform SAGA.Text/Conference Paper