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Outdoor thermal comfort simulation simulations rely on the mean radiant temperature (MRT) seen by pedestrians as an important input that remains difficult to compute. Especially for large urban models, computing relevant surface temperatures and radiation fluxes that make up the MRT is a daunting task in terms of simulation setup and the computational overhead. We propose a new algorithm to estimate exterior surface temperatures of building facades, roofs, and ground surfaces in an arbitrary urban 3D model. The algorithm discretizes all model surfaces and clusters them by material properties and sky and sun exposure to reduce computational complexity. The model setup is fully automated, and the algorithm is implemented in the popular Rhino3d CAD environment. We demonstrate the accuracy of the algorithm by comparing both the resulting external surface temperatures against a high-fidelity simulation and the final MRT against real-world measurements. We report an RMSE of 1.8 °C and 2.0 °C, respectively, while reducing simulation times by a factor of ~80. Envisioned applications of the algorithm range from rapid microclimate simulations in fast-paced urban design processes to large scale urban comfort evaluation of existing cities. • New algorithm to predict exterior surface temperatures and MRT in an arbitrarily complex urban 3D model. • The algorithm is utilizing K-Means clustering to reduce simulation times by a factor of 80. • We report an RMSE of 1.8°C for surface temperature predictions of a full year. • Importance of long-wave radiation exchange is analyzed. • Largest heterogeneity in external surface temperature is reported for horizontal clusters close to obstacles and south-facing clusters. [ABSTRACT FROM AUTHOR]