Abstract. The escalating urban heat, driven by climate change and urbanization, poses significant threats to residents’ health and urban climate resilience. The coupled radiative-convective-conductive heat transfer across complex urban geometries makes it challenging to identify the primary causes of urban heat and develop mitigation strategies. To address this challenge, we develop a GPU-accelerated Urban Surface Temperature model (GUST) through CUDA architecture. To simulate the complex radiative exchanges and coupled heat transfer processes, we adopt Monte Carlo method, leveraging GPUs to overcome its computational intensity while retaining its high accuracy. Radiative exchanges are resolved using a reverse ray tracing algorithm, while the conduction-radiation-convection mechanism is addressed through a random walking algorithm. The validation is carried out using the Scaled Outdoor Measurement of Urban Climate and Health (SOMUCH) experiment, which features a wide range of urban densities and offers high spatial and temporal resolution. This model exhibits notable accuracy in simulating urban surface temperatures and their temporal variations across different building densities. Analysis of the surface energy balance reveals that longwave radiative exchanges between urban surfaces significantly influence model accuracy, whereas convective heat transfer has a lesser impact. To demonstrate the applicability of GUST, it is employed to model transient surface temperature distributions at complex geometries on a neighborhood scale. Leveraging the high computational efficiency of GPU, the simulation traces 10⁵ rays across 2.3×10⁴ surface elements in each time step, ensuring both accuracy and high-resolution results for urban surface temperature modeling.