A High-Resolution (500m) Daily 2m Air Temperature Dataset via Causal Deep Learning (2000–2023)

This dataset provides a high-resolution daily 2-meter air temperature (T2m) record spanning from 2000 to 2023, generated utilizing advanced causal deep learning methodologies. It features a spatial resolution of 500 meters and a daily temporal resolution, offering highly detailed thermal data essential for micrometeorological studies, urban climate analysis, and agricultural planning. By employing a causal deep learning framework rather than relying solely on traditional statistical correlations, this dataset explicitly models the underlying cause-and-effect mechanisms within the climate system. This innovative approach ensures a physically robust, accurate, and reliable representation of complex spatial and temporal temperature dynamics. The development of this dataset was financially supported by the Wuhan Natural Science Foundation Exploration Project (Chenguang Project) under grant number 2024040801020279. Researchers and practitioners utilizing this temperature data for academic or applied studies are kindly requested to properly cite the dataset and acknowledge this funding source in their related publications and research outputs.