Urban environments experience amplified thermal stress due to the climate change, leading to increased health risks during extreme temperature events. Existing numerical weather prediction systems often lack the spatial resolution required to capture this phenomenon. This study assesses the efficacy of a coupled modeling system, the numerical weather prediction AROME model and the land-surface model SURFace EXternalisée in a stand alone mode (SURFEX-SA), in forecasting air temperatures at high resolutions ($2.5$km to $100$m) across four Austrian cities (Vienna, Linz, Klagenfurt and Innsbruck). The system is updated with the, according to the author’s knowledge, most accurate land use and land cover input to evaluate the added value of incorporating detailed urban environmental representations. The analysis focuses on the years 2019, 2023, and 2024, examining both summer and winter seasons. SURFEX-SA demonstrates improved performance in specific scenarios, particularly during nighttime in rural and suburban areas during the warmer season. By comprehensively analyzing this prediction system with operational and citizen weather stations in a deterministic and probabilistic mode across several time periods and various skill scores, the findings of this study will enable readers to determine whether high-resolution forecasts are necessary in specific use cases.