Evaluating the potential for heat warning systems to account for intra-urban variability

Urban heat increasingly threatens public health as climate change intensifies heatwaves frequency, duration and severity. Germany’s current county-level heat warning system might overlook urban–rural and intra-urban differences in heat exposure, limiting its usefulness for local action. This study developed and evaluated neighborhood-scale Pedestrian Heat Stress Products (PHSP) using high-resolution Universal Thermal Climate Index (UTCI) predictions from a machine learning framework. The Human Thermal Comfort Neural Network (HTC-NN) was used to generate hourly 1 × 1 m UTCI maps for Freiburg, Germany, during June–August 2023, accounting for buildings, vegetation, and street geometry. Predictions were aggregated to neighborhood scale (512 × 512 m), excluding buildings to represent pedestrian conditions. Daytime hours exceeding UTCI thresholds of 32 °C (strong heat stress) and 38 °C (very strong) were summed per 1 × 1 m cell and averaged by neighborhood. PHSP Level 1 was assigned when ≥2 hours exceeded 32 °C UTCI, Level 2 when ≥30 minutes exceeded 38 °C UTCI. PHSP demonstrated robust accuracy against official warnings (70.1% urban) and weather station data (82.0% urban). ... mehrComparing PHSP with official warnings revealed major differences: the current system missed 24 urban warning days in 2023. Urban areas experienced 1.2 times more Level 1 and 1.8 times more Level 2 days than rural surroundings, highlighting urban heat amplification and strong spatial heterogeneity linked to morphology. This study shows that PHSPs allow for differentiated warnings and can be used to transition from coarse, county-level to high-resolution, neighborhood-scale warning systems to deliver actionable, location-specific heat risk information.