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 specifically covering Hubei Province, China, 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 regional thermal data essential for micrometeorological studies, urban climate analysis, and agricultural planning across the province. 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 tailored to the specific geographical and climatic conditions of Hubei Province. 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.本数据集提供了覆盖中国湖北省的高分辨率日度2米气温（2-meter air temperature，T2m）记录，时间跨度为2000年至2023年。本数据集采用先进的因果深度学习方法生成，空间分辨率达500米，时间分辨率为日度，可提供高细节的区域热力数据，适用于该省的微气象研究、城市气候分析与农业规划等场景。相较于仅依赖传统统计相关性的方法，本数据集采用因果深度学习框架，对气候系统内的潜在因果机制进行显式建模。该创新方法可针对湖北省特定的地理与气候条件，精准且可靠地复现复杂的时空温度动态过程，且具备物理合理性。 本数据集的研发得到了武汉市自然科学基金探索项目（晨光计划）资助，项目编号为2024040801020279。恳请使用该气温数据开展学术或应用研究的科研人员与从业者，在相关发表成果与研究产出中，正确引用本数据集并致谢本资助来源。