Transfer learning reveals large discrepancies between air and land surface temperatures in cities

Understanding of urban weather and extreme events remains severely limited by data poverty resulting from a dearth of true urban weather stations. As a result, land surface temperature (Ts), obtained from remote sensing platforms, has been widely used as a stand-in for near-surface air temperature (Ta) despite their fundamental differences, especially in urban areas. Although Ts provides important scientific insights and practical utility for studying the urban thermal environment, this substitution risks introducing large uncertainties and biased characterization of impact-relevant urban heat stress. Here we develop an urban transfer-learning framework (U-TL) to address this critical gap and to provide urban high-resolution air temperature (U-HAT) data at large scales across the contiguous United States (CONUS). U-TL demonstrates high accuracy and strong robustness in predicting urban Ta, even with limited training data. The resulting U-HAT is a high-resolution urban Ta dataset capable of accurately reproducing observed and well-established urban climatology. U-HAT reveals substantial Ts–Ta discrepancies and therefore cautions the use of Ts to characterize urban heat. We show that satellite-measured Ts substantially overestimates both urban heat stress magnitude and intra-city spatial variability, which have consequential implications for urban heat exposure, vulnerability, and adaptation policy making. This study develops a transfer-learning method to estimate high-resolution urban air temperature across the contiguous United States and shows that satellite land surface temperature substantially overestimates heat stress and temperature variability in those cities.