The Application of Machine Learning Algorithms to the Global Forecast of Temperature-Humidity Index with High Temporal Resolution

Climate change poses a significant threat to agriculture, with potential impacts on food security, economic stability, and human livelihoods. Dairy cattle, a crucial component of the livestock sector, are particularly vulnerable to heat stress, which can adversely affect milk production, immune function, feed intake, and in extreme cases, lead to mortality. The Temperature Humidity Index (THI) is a widely used metric to quantify the combined effects of temperature and humidity on cattle. However, most studies estimate THI using daily-level data, which fails to capture the full extent of daily thermal load and cumulative heat stress, especially during nights when cooling is inadequate. To address this limitation, we developed a machine learning approach to temporally downscale daily climate data to hourly THI values. Utilizing historical ERA5 reanalysis data, we trained an XGBoost model and generated hourly THI datasets for 12 NEX-GDDP-CMIP6 climate models under two emission scenarios (SSP2-4.5 and SSP5-8.5) extending to the end of the century. This high-resolution THI data provides a more accurate assessment of heat stress in dairy cattle, enabling better predictions and management strategies to mitigate the impacts of climate change on this vital agricultural sector.