Urban microclimate prediction is crucial for various fields, including Building Performance Simulation (BPS), outdoor thermal comfort, building life cycle, and residential health. Existing methods involve using classical weather file data, such as Typical Meteorological Years (TMY), or machine learning techniques for time-based forecasting. However, the incorporation of both spatial and temporal dimensions and land use/land cover (LULC) data is seldom considered. This paper proposes a novel a...| Urban Analytics Lab | Singapore