Near-surface air temperature is a key physical property of the Earth's surface. Although weather stations offer continuous monitoring and satellites provide broad spatial coverage, no single data source offers seamless data in a spatiotemporal fashion. Here, we propose a data-driven, physics-guided deep learning approach to generate hourly air temperature data at 2 km resolution over the contiguous United States. The approach, called Amplifier Air-Transformer, first reconstructs GOES-16 surface temperature data obscured by clouds. It does so through a neural network encoded with the annual temperature cycle, incorporating a linear term to amplify ERA5 temperature values at finer scales and convolutional layers to capture spatiotemporal variations. Then, another neural network transforms the reconstructed surface temperature into air temperature by leveraging its latent relationship with key Earth surface properties. The approach is further enhanced with predictive uncertainty estimation through deep ensemble learning to improve reliability. The proposed approach is built and tested on 77.7 billion surface temperature pixels and 155 million air temperature records from weather stations across the contiguous United States (2018-2024), achieving hourly air temperature mapping accuracy of 1.93 C in station-based validation. The proposed approach streamlines surface temperature reconstruction and air temperature prediction, and it can be extended to other satellite sources for seamless air temperature monitoring at high spatiotemporal resolution. The generated data of this study can be downloaded at https://doi.org/10.5281/zenodo.15252812, and the project webpage can be found at https://skrisliu.com/HourlyAirTemp2kmUSA/.

In this study, we firstly introduce a method that converts CityGML data into voxels which works efficiently and fast in high resolution for large scale datasets such as cities but by sacrificing some building details to overcome the limitations of previous voxelization methodologies that have been computationally intensive and inefficient at transforming large-scale urban areas into voxel representations for high resolution. Those voxelized 3D city data from multiple cities and corresponding air temperature data are used to develop a machine learning model. Before the model training, Gaussian blurring is implemented on input data to consider spatial relationships, as a result the correlation rate between air temperature and volumetric building morphology is also increased after the Gaussian blurring. After the model training, the prediction results are not just evaluated with Mean Square Error (MSE) but some image similarity metrics such as Structural Similarity Index Measure (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS) that are able to detect and consider spatial relations during the evaluation process. This trained model is capable of predicting the spatial distribution of air temperature by using building volume information of corresponding pixel as input. By doing so, this research aims to assist urban planners in incorporating environmental parameters into their planning strategies, thereby facilitating more sustainable and inhabitable urban environments.