Heat pattern of cities is characterized by its higher temperature than the surrounding environments, and cities are vulnerable places to heat-induced risk because of its dense population. Therefore, fast/accurate heat risk assessment is desired for mitigation plans and sustainable community management. This paper introduces a probabilistic model to forecast the meso-scale surface temperature at a relatively low computational cost, as an alternative to computationally intensive Numerical Weather Prediction (NWP) models. After calibrating the model, we integrate the model into the probabilistic risk analysis framework to estimate extreme temperature distribution around the cities. The surrogate model expands its applicability, providing insights on the future risk and various statistical inferences, being integrated with the framework.