High Resolution City-Scale Climate Projections of Urban Heat Stress based on an Deep Learning Approach

Urban areas are increasingly vulnerable to climate change impacts, particularly heatwaves, due to their unique characteristics. However, the influence of urban form and land cover on future outdoor thermal comfort remains insufficiently quantified in existing climate models. In this study, we present UHTC-NN, a novel deep learning model designed to predict human thermal comfort (UTCI) in complex urban environments at an unprecedented 1-meter spatial resolution. UHTC-NN provides rapid, high-resolution predictions of pedestrian-level UTCI fields, enabling systematic and quantitative analysis of urban heat stress.

We demonstrate the capabilities of UHTC-NN by downscaling a CMIP5 regional climate model ensemble to 1-meter resolution for a 5.0 km x 2.5 km area in Freiburg, Germany. The results reveal significant increases in heat stress hours under the RCP4.5 and RCP8.5 scenarios, with the climate signal emerging as the dominant driver. Our analysis highlights substantial intra-urban variability in heat stress hours for both the reference period (1990–2019) and projected future changes (2070–2099), emphasizing the critical need for high-resolution prediction models like UHTC-NN.

Additionally, our findings reveal distinct day-night patterns: future daytime heat stress increases more uniformly across the city, whereas nighttime heat stress exhibits greater spatial heterogeneity, driven by factors such as shading, building density, and land cover. The high-resolution UTCI predictions generated by UHTC-NN represent a significant advancement in data-driven heat stress modeling, offering a holistic understanding of climate change impacts, complex urban structures, and diurnal variations.