Extending Urban Thermal Signal Downscaling Across Europe

Intra-urban air temperature is highly heterogeneous, and coarse-resolution products cannot resolve the neighbourhood-scale variability that urban heat management requires. The objective of this study is to assess whether the Urban Thermal Signal Downscaling (UTS-D) methodology, previously developed and tested over Danish Functional Urban Areas (FUAs) with the DAnish ReAnalysis (DANRA), can be re-implemented with a pan-European reanalysis and retrained in cities with contrasting climates, and to quantify the incremental value of a vegetation-seasonality predictor. The approach is applied to four European FUAs spanning contrasting climate zones, i.e., Copenhagen (Denmark), Lisbon (Portugal), Seville (Spain) and Athens (Greece). Two modifications were introduced: the regional weather component was replaced with the pan-European Copernicus Regional Reanalysis (CERRA), enabling application beyond Denmark, and a monthly climatological Normalized Difference Vegetation Index (NDVI) was added as a non-fixed predictor to capture the seasonality of vegetation cooling. A Random Forest model was trained to predict the residual bias between CERRA 2 m air temperature and quality-controlled observations from citizen-owned weather stations, combined with static landscape and urban predictors. Using a 2019–2022 training/cross-validation strategy and an independent 2023 test year, the downscaled model substantially outperformed raw CERRA in all four cities, reducing the test-year mean absolute error from 1.02–1.99 °C to 0.69–1.06 °C (a 32–55% reduction) and reaching R2 of 0.94–0.98. Absolute error reductions were particularly relevant on hot days (HD) (0.52–1.21 °C); however, such periods are also the ones during which CERRA errors tend to be largest, indicating that the hot-day added value mainly reflects the correction of a larger reanalysis bias. NDVI provided only a marginal additional improvement (≤0.02 °C; ≤2%), statistically significant but negligible in practical terms. Errors were lowest in Copenhagen, which has the densest citizen-station network, and highest in Seville, which has the sparsest; however, a network-thinning experiment showed that station density explains only part of these differences, pointing to the combined role of station density, the magnitude of the reanalysis error, climate and urban morphology. These results show that UTS-D is methodologically transferable, i.e., it can be retrained successfully with CERRA wherever sufficiently dense citizen-station observations exist, and provide a basis for future heat-health and urban adaptation applications.