HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020

Abstract. Human-perceived thermal comfort (known as human-perceived temperature) measures the combined effects of multiple meteorological factors (e.g., temperature, humidity, and wind speed) can be aggravated under influences global warming local human activities. With most rapid urbanization largest population, China is being severely threatened by aggravating stress. However, variations stress in at a fine scale have not been fully understood. This gap mainly due to lack high-resolution gridded dataset indices. Here, we generated first high spatial resolution (1 km) monthly index collection (HiTIC-Monthly) over during 2003–2020. In this collection, 12 commonly used indices were Light Gradient Boosting Machine (LightGBM) learning algorithm from multi-source data, including land surface topography, cover, population density, impervious fraction. Their accuracies comprehensively assessed based on observations 2419 weather stations across mainland China. The results show that our has desirable accuracies, with mean R2, root square error, absolute error 0.996, 0.693 ∘C, 0.512 respectively, averaging Moreover, data exhibit agreements temporal dimensions, demonstrating broad applicability dataset. A comparison two existing datasets also suggests describe more explicit distribution information, showing great potentials fine-scale intra-urban) studies. Further investigation reveals nearly all increasing trends parts increase especially significant North China, Southwest Tibetan Plateau, Northwest spring summer. HiTIC-Monthly publicly available Zenodo https://doi.org/10.5281/zenodo.6895533 (Zhang et al., 2022a).