Articles | Volume 17, issue 3
https://doi.org/10.5194/essd-17-1153-2025
https://doi.org/10.5194/essd-17-1153-2025
Data description paper
 | 
19 Mar 2025
Data description paper |  | 19 Mar 2025

Global projections of heat stress at high temporal resolution using machine learning

Pantelis Georgiades, Theo Economou, Yiannis Proestos, Jose Araya, Jos Lelieveld, and Marco Neira

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Short summary
Climate change is posing increasing challenges in the dairy cattle farming sector, as heat stress adversely affects the animals' health and milk production. To accurately assess these impacts, we developed a machine learning model to downscale daily climate data to hourly Temperature Humidity Index (THI) values. We utilized historical weather data to train our model and applied them to future climate projections, under two climate scenarios.
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