ESSDD Earth System Science Data Discussions ESSDD Earth Syst. Sci. Data Discuss. 1866-3591 Göttingen, Germany 10.5194/essd-2026-547 AguaTrack-ARCO-SA: A 30-year, high-resolution atmospheric moisture tracking dataset for South America Hung Ho Tin https://orcid.org/0000-0002-9301-1694 1 Lin Sung Che 1 Tseng Kai-Chih 2 Weng Wei 3 Wang Li-Pen https://orcid.org/0000-0003-0981-8397 1 4 Department of Civil Engineering, National Taiwan University, Taipei 10617, Taiwan Department of Atmospheric Sciences, National Taiwan University, Taipei 10617, Taiwan Department of Geography, National Taiwan University, Taipei 10617, Taiwan Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK 05 08 2026 2026 1 23 Copyright: © 2026 Ho Tin Hung et al. 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://essd.copernicus.org/preprints/essd-2026-547/ The full text article is available as a PDF file from https://essd.copernicus.org/preprints/essd-2026-547/essd-2026-547.pdf

Atmospheric moisture recycling is a key component of the hydrological cycle, linking evapotranspiration to downwind precipitation. However, existing moisture tracking datasets are often limited by coarse spatial resolution, short temporal coverage, or high computational demands, constraining their use for subnational-scale and short-term event analyses. Here, we present AguaTrack-ARCO-SA, a 30-year (1990&ndash;2019), daily, 0.25&deg; resolution atmospheric moisture backtracking dataset for South America, generated using the Eulerian WAM2layers v3 model driven by ERA5 reanalysis data. The dataset independently tracks moisture sources for approximately 25,000 land grid cells, providing detailed source&ndash;sink relationships at department to continental scales. The primary output variable, tracked evaporation, quantifies the contribution of each source grid cell to precipitation at each target location, alongside diagnostic variables for moisture gains, losses, and tagged precipitation. To facilitate efficient access, we compress the &sim;230 TB of raw tracking output into an &sim;8 TB Analysis-Ready, Cloud-Optimized (ARCO) product using Zarr storage. This structure enables scalable querying and analysis through cloud-based or local workflows without requiring high-performance computing resources. The dataset is openly available on Hugging Face (<a href="https://doi.org/10.57967/hf/8650" target="_blank" rel="noopener">https://doi.org/10.57967/hf/8650</a>) and the accompanying code and tutorials on GitHub (<a href="https://github.com/NTU-CompHydroMet-Lab/AguaTrack-ARCO-SA-Tutorial" target="_blank" rel="noopener">https://github.com/NTU-CompHydroMet-Lab/AguaTrack-ARCO-SA-Tutorial</a>), supporting reproducibility and reuse. This dataset provides a high-resolution, long-term resource for analysing atmospheric moisture transport, precipitationsheds, and hydroclimatic variability across South America.