Quantifying Intra-Zonal Heatwave Variability Using Local Climate Zone Subclasses and Machine Learning: A Cross-Climate Study of Bari (Italy) and Amsterdam (Netherlands)

This study analyzed the integration of Local Climate Zones (LCZ) with machine learning (ML) has advanced urban heat research and quantifying intra-zonal heatwave variability within built LCZ classes (1–10). However, most studies remain constrained to single-city analyses and treat LCZ classes as internally homogeneous. Heatwave events (2013–2025) were identified from the weather stations data through applying a WMO definition for Bari and KNMI local criteria for Amsterdam. LCZ maps for 2025 were derived at 500 m resolution, while morphological subclasses were defined at 100 m and aggregated to capture intra-zonal heterogeneity using a WUDAPT-GIS approach. For Amsterdam, quantify intra-zonal thermal heterogeneity in built-up LCZ classes, parameterized by key parameters like built-up index, impervious surface fraction, and surface albedo. In parallel, the heatwave-driven thermal behaviour and relative UHI dominance of natural LCZ subclasses were explicitly analysed to capture vegetation-mediated cooling dynamics and their interaction with urban heat island and heatwaves events. A Random Forest model estimated LCZ-specific land surface temperature (LST) during heatwaves, with SHAP analysis used to quantify predictor importance.

We find that model performance is robust (R² = 0.79 to 0.85) in different LCZs during heatwaves events in the both cities. In Bari, LCZ-based modelling demonstrates strong performance (R² ≈ 0.81; RMSE ≈ 2.0 °C), with compact classes showing pronounced heat storage and nocturnal heat retention. In Amsterdam, the inclusion of LCZ subclasses reveals additional intra-zonal variability (up to 0.7–2 °C) and alters the spatial extent of high-temperature areas (15% to 20%). These results indicate that LCZ classes are not thermally homogeneous and that incorporating intra-zonal morphological detail improves to understand the heatwave hazards in built-up areas and spatial heat-risk assessment.