Project: effectS of clImate chanGe oN dAiry cattle - The SIGNAL project is dedicated to quantifying the impacts of climate change on dairy cattle, with a primary focus on the challenges posed by heat stress due to rising temperatures and humidity levels. Our mission is to safeguard the health and productivity of dairy cattle in the face of a changing climate. Heat stress in dairy cattle is a critical issue, as it can significantly reduce milk production, affect reproduction, and compromise overall animal welfare. Through SIGNAL, we seek to develop robust and scientifically validated thresholds for heat stress, enabling farmers and industry stakeholders to better manage and mitigate these adverse effects. To achieve our goals, we leverage the expertise of a multidisciplinary team comprising computational scientists, climate change experts, statisticians, and agriculture scientists. By integrating advanced computational methods with real-world measurements from dairy cattle, we aim to create accurate and actionable insights. Our collaborative approach involves partnering with leading research institutions, agricultural organisations, and industry leaders. Together, we collect and analyse data on temperature, humidity, and cattle health, using sophisticated statistical models and computational algorithms to identify critical thresholds where heat stress becomes detrimental. The outcomes of the SIGNAL project will provide dairy farmers with precise tools and guidelines to optimise cattle management practices under varying climatic conditions. This not only helps in enhancing the welfare of the animals but also ensures sustainable productivity and resilience in the dairy industry. In summary, SIGNAL is at the forefront of addressing climate-induced challenges in dairy farming through innovative research and collaboration. By setting new standards for heat stress management, we aim to protect dairy cattle and support the industry in adapting to the realities of climate change. Summary: 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 (https://doi.org/10.7917/OFSG3345) 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 Chen et al. 2016) algorithm, chosen for its efficiency and capability to handle large datasets, to train models using historical hourly data from the ERA5 reanalysis dataset (Hersbach et al. 2020). 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.