EGU26-9714, updated on 14 Mar 2026
https://doi.org/10.5194/egusphere-egu26-9714
EGU General Assembly 2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
MSWEP V3: Machine Learning-Powered Global Precipitation Estimates at 0.1° Hourly Resolution (1979–Present)
1,1,3,3,1,4,5,7,8,9,10,11,12- 1Thuwal, Saudi Arabia (hylke.beck@gmail.com)
- 2Department of Civil Engineering, College of Engineering, King Saud University, Riyadh, Saudi Arabia
- 3Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium
- 4ECMWF, Reading, United Kingdom
- 5Fenner School of Environment & Society, Australian National University, Canberra, ACT, Australia
- 6CSIRO Environment, Canberra, Australian Capital Territory, Australia
- 7Centre of Studies in Resources Engineering, IIT Bombay, Mumbai, India
- 8Centre for Climate Studies, IIT Bombay,Mumbai, India
- 9School of Engineering, Newcastle University, Newcastle upon Tyne, UK
- 10Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego, San Diego, California
- 11School of Geography and the Environment, University of Oxford, Oxford, UK
- 12School of Geography and Environmental Science, University of Southampton, Southampton, UK
How to cite: Beck, H., Wang, X., Alharbi, R., Baez-Villanueva, O., Miralles, D., Ma, J., Xu, S., McCabe, M., Pappenberger, F., van Dijk, A., McVicar, T., Karthikeyan, L., Fowler, H., Pan, M., and Gebrechorkos, S.: MSWEP V3: Machine Learning-Powered Global Precipitation Estimates at 0.1° Hourly Resolution (1979–Present), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9714, https://doi.org/10.5194/egusphere-egu26-9714, 2026.