This study investigates the impact of urban tree stock on the local climate in the center of Eberswalde, Germany. Specifically, the effect of trees on shadow patterns and mean radiant temperature (Tmrt) are analyzed. First, aerial LiDAR data of the research area were classified into vegetation and building classes. LiDAR points under 4 m were excluded from the classification process, except for ground points and a few trees on the market square. Out of the classified LiDAR datasets two digital surface models were generated: one representing canopy cover and the other, building and ground surfaces. These models, along with land cover data and modified local weather data, were input into the SOLWEIG toolkit (UMEP extension, QGIS) to simulate Tmrt on an exceptionally hot summer afternoon. Maps showing shadow patterns, average Tmrt, and time over certain Tmrt thresholds were produced and analyzed, with high Tmrt serving as a proxy for thermal stress in vulnerable groups. The resulting maps highlight areas of elevated thermal stress and demonstrate the cooling effect of existing tree stock. Furthermore, this paper serves as a case study for city planners with access to quality LiDAR data looking to quantify or predict the effect of current and/or future trees or buildings on local climate in normal or extreme weather scenarios.