air temperature dataset generated in "Space-time deep hybrid boosting learning for investigating day-night hourly seamless air temperature distribution from FY-4A over China"

Greenhouse gas emissions have driven global warming and increased the frequency of intense heatwaves during both daytime and nighttime, making it crucial to accurately and timely monitor air temperature (Ta) for heatwave exposure assessment. However, existing studies mostly failed to adequately capture day-night consecutive Ta patterns, as they have focused solely on daytime values and were limited by their estimated result with coarse temporal resolutions and insufficient spatiotemporal characteristics. To address these limitations, we develop a Space-Time Deep Hybrid Boosting (ST-DHB) model to investigate day-night hourly seamless 0.04-degree Ta distribution from Fengyun-4A across China. Validation results demonstrate that our model performs well in the study area, with the R²/RMSE values of 0.946/2.593°C during daytime and 0.958/2.218°C during nighttime. Moreover, the model achieves better metrics compared to several widely used machine learning methods and outperforms the models reported in recent studies. The Ta estimation results display continuous spatial details and accurately capture the hourly and seasonal Ta variations. Notably, we find that urban areas and farmlands with large population experience more severe high-temperature exposure at both spatial and temporal scales, potentially indicating larger threats of heatwaves to human health. The estimated Ta can effectively support daytime and nighttime heatwave exposure assessment in our study, which reveals significant geographical, seasonal, and diurnal disparities of heatwaves across China. This study may provide reliable estimation model and Ta dataset for assessing health risks of day-night composite heatwave exposure, potentially benefitting the heatwave-exposed population in the future.