The experiment conducted aimed to enhance the temporal resolution of climate projections for agricultural applications by using machine learning to downscale daily NEX-GDDP-CMIP6 climate data to hourly Temperature Humidity Index (THI) values. The THI is a critical metric for assessing heat stress in dairy cattle, which is a significant concern under changing climatic conditions. We utilized the Extreme Gradient Boost (XGBoost) algorithm, chosen for its efficiency and capability to handle large datasets, to train models using historical hourly data from the ERA5 reanalysis dataset. The trained models were then applied to generate hourly THI projections from 2020 to 2100 across 12 climate models under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). The focus was exclusively on land areas, with a spatial grid resolution of 0.25 degrees, ensuring the relevance and applicability of the data for agricultural purposes. The result is a comprehensive, high-resolution dataset that provides detailed insights into the future impacts of heat stress on dairy cattle, facilitating better planning and mitigation strategies in the agricultural sector.
This dataset contains data for the GFDL-CM4 climate model, SSP2-45 emission scenario.