How can we improve data integration to enhance urban air temperature estimations?

High-resolution urban air temperatures are indispensable for analysing excess mortality during heatwaves. As a crucial method for obtaining high-resolution data, multi-source data integration has been widely used in urban temperature estimations. However, current research predominantly focuses solely on integrating official weather station observations, satellite products, and reanalysis datasets. Despite the significant cooling effect of rainfall on air temperatures, no studies have explored the contribution of rainfall-related variables to high-resolution air temperature estimations. Additionally, due to the scarcity of official weather stations, quantifying the impact of station density remains an underexplored research direction. To tackle these challenges, we innovatively integrated satellite products, reanalysis datasets, and weather radar data with air temperature observations from crowdsourced weather stations. Using genetic programming, we developed statistical downscaling models to estimate high spatiotemporal resolution (1-km, hourly) air temperatures in London during the summers of 2019 and 2022. The models achieved RMSEs of 1.694 °C (2019) and 1.785 °C (2022), R-squared values of 0.867 and 0.862, and MAEs of 1.276 °C and 1.278 °C, respectively. Notably, the accuracy of the models was found to improve with increased weather station density, particularly when the density was below 0.5 stations per 100 km². Moreover, high-resolution rainfall observations significantly impacted the accuracy of air temperature estimations, second only to elevation, highlighting the potential of integrating radar data. These findings can provide valuable insights for scholars aiming to improve data integration for enhancing urban air temperature estimations.