HiTIC-Monthly: A High Spatial Resolution (1 km×1 km) Monthly Human Thermal Index Collection over China from 2003 to 2020

Abstract

Abstract. Human thermal comfort measures the combined effects of temperature, humidity, and wind speed, etc., and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generate the first high spatial resolution (1 km1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China from 2003 to 2020. In this collection, 12 commonly used thermal indicators are generated by the LGBM machine learning algorithm from multi-source gridded data, including MODIS land surface temperature, topography, land cover and land use, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable performance, with mean R2, root mean square error, mean absolute error, and bias of 0.996, 0.693 °C, 0.512 °C, and 0.003 °C, respectively, by averaging the 12 indicators. Moreover, the predictions exhibit high agreements with observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. The comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) study. Further investigation reveals that nearly all indicators exhibit increasing trends in most parts of China during the year 2003~2020. The increase is especially stronger in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, and in the spring and summer seasons. The HiTIC-Monthly dataset is publicly available via https://zenodo.org/record/6895533 (Zhang et al., 2022a).

Effects of urbanization on multiple human perceived temperatures in South China

“…It plays an important role in urban environmental meteorological services. However, there is still no universally accepted or unified indicator of HPT currently (Blazejczyk et al, 2012;Li et al, 2018;Zhang et al, 2022). On the one hand, previous studies on extreme high temperatures mainly focused on near-surface air temperature, such as daily maximum and minimum temperatures, yet few considered the combined effects of multiple environmental factors on HPT (Liu et al, 2018;Wang et al, 2021b).…”