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Climate change affects temperature, magnitude, and also the duration of the Urban Heat Island (UHI), which has severe impacts on the environment, communities, and the peoples lives. This study evaluates UHI formation in the city of Tehran (Iran) using a reliability framework, which is able to include related uncertainties into the modeling procedure. First, 2 km air temperature field from Weather and Research Forecasting (WRF) model is downscaled to a 50 m grid spacing using a probabilistic downscaling method, which combines inverse distance weighting (IDW) interpolation and a Bayesian regression model. This downscaling method not only can produce a long record of the fine resolution of the temperature but also improves the WRF temperature outputs' accuracy. The downscaled temperature field is then applied for UHI assessment using the first order reliability methods (MVFOSM and FORM). The results indicate that although the maximum intensity of UHI occurs in the warm months, there is a higher probability for UHI formation in the cold months. Furthermore, it is more probable that UHI forms during the night. UHI formation probabilities indicate that two districts in the central part of the city, mainly composed of old dense buildings, are more exposed to UHI in the warm and the cold months. District 1 that has the lowest probability of UHI formation is located in the most elevated part of the city (close to the Alborz mountain range). Results are invaluable for policy making process and risk management plans to assess severity levels of UHI in different regions based on the probability of event's occurrence.