Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-4983-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-4983-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Oct 2025
Research article |
| 08 Oct 2025
| 08 Oct 2025
Saudi Rainfall (SaRa): hourly 0.1° gridded rainfall (1979–present) for Saudi Arabia via machine learning fusion of satellite and model data
Xuetong Wang
Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Raied S. Alharbi
Department of Civil Engineering, College of Engineering, King Saud University, Riyadh, Saudi Arabia
Oscar M. Baez-Villanueva
Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium
Amy Green
School of Engineering, Newcastle University, Newcastle upon Tyne, UK
Tyndall Centre for Climate Change Research, Newcastle University, Newcastle upon Tyne, UK
Matthew F. McCabe
Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Yoshihide Wada
Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Albert I. J. M. Van Dijk
Fenner School of Environment & Society, Australian National University, Canberra, ACT, Australia
Muhammad A. Abid
Atmospheric, Oceanic and Planetary Physics (AOPP), Department of Physics, University of Oxford, Oxford, UK
National Centre for Atmospheric Science (NCAS), Leeds, UK
Hylke E. Beck
CORRESPONDING AUTHOR
Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
GloH2O LLC, Princeton, NJ, USA
Related authors
No articles found.
Fan Yang, Maike Schumacher, Leire Retegui-Schiettekatte, Albert I. J. M. van Dijk, and Ehsan Forootan
Geosci. Model Dev., 18, 6195–6217, https://doi.org/10.5194/gmd-18-6195-2025, https://doi.org/10.5194/gmd-18-6195-2025, 2025
Short summary
Short summary
Satellite gravimetry enables direct measurement of total water storage (TWS), a capability that was previously unattainable. In this study, we present an open-source land data assimilation system with global hydrological model, which temporally, vertically, and laterally dis-aggregates satellite-based TWS. This study provides a practical framework establishing operational water management with current and future satellite gravity missions.
Mateo Barco Largo, Meshal Alarifi, Sami D. Almalki, Shauna K. Rees, Benjamin P. Y.-H. Lee, Ahmed H. Mohamed, Abdalsamad Aldabaa, Kaoru Kakinuma, and Yoshihide Wada
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-G-2025, 183–188, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-183-2025, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-183-2025, 2025
Hannes Müller Schmied, Simon Newland Gosling, Marlo Garnsworthy, Laura Müller, Camelia-Eliza Telteu, Atiq Kainan Ahmed, Lauren Seaby Andersen, Julien Boulange, Peter Burek, Jinfeng Chang, He Chen, Lukas Gudmundsson, Manolis Grillakis, Luca Guillaumot, Naota Hanasaki, Aristeidis Koutroulis, Rohini Kumar, Guoyong Leng, Junguo Liu, Xingcai Liu, Inga Menke, Vimal Mishra, Yadu Pokhrel, Oldrich Rakovec, Luis Samaniego, Yusuke Satoh, Harsh Lovekumar Shah, Mikhail Smilovic, Tobias Stacke, Edwin Sutanudjaja, Wim Thiery, Athanasios Tsilimigkras, Yoshihide Wada, Niko Wanders, and Tokuta Yokohata
Geosci. Model Dev., 18, 2409–2425, https://doi.org/10.5194/gmd-18-2409-2025, https://doi.org/10.5194/gmd-18-2409-2025, 2025
Short summary
Short summary
Global water models contribute to the evaluation of important natural and societal issues but are – as all models – simplified representation of reality. So, there are many ways to calculate the water fluxes and storages. This paper presents a visualization of 16 global water models using a standardized visualization and the pathway towards this common understanding. Next to academic education purposes, we envisage that these diagrams will help researchers, model developers, and data users.
Robert Reinecke, Annemarie Bäthge, Ricarda Dietrich, Sebastian Gnann, Simon N. Gosling, Danielle Grogan, Andreas Hartmann, Stefan Kollet, Rohini Kumar, Richard Lammers, Sida Liu, Yan Liu, Nils Moosdorf, Bibi Naz, Sara Nazari, Chibuike Orazulike, Yadu Pokhrel, Jacob Schewe, Mikhail Smilovic, Maryna Strokal, Yoshihide Wada, Shan Zuidema, and Inge de Graaf
EGUsphere, https://doi.org/10.5194/egusphere-2025-1181, https://doi.org/10.5194/egusphere-2025-1181, 2025
Short summary
Short summary
Here we describe a collaborative effort to improve predictions of how climate change will affect groundwater. The ISIMIP groundwater sector combines multiple global groundwater models to capture a range of possible outcomes and reduce uncertainty. Initial comparisons reveal significant differences between models in key metrics like water table depth and recharge rates, highlighting the need for structured model intercomparisons.
Taher Kahil, Safa Baccour, Julian Joseph, Reetik Sahu, Peter Burek, Jia Yi Ng, Samar Asad, Dor Fridman, Jose Albiac, Frank A. Ward, and Yoshihide Wada
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-238, https://doi.org/10.5194/gmd-2024-238, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study presents the development of the global version of the ECHO hydro-economic model for assessing the economic and environmental performance of water management options. This improved version covers a large number of basins worldwide, includes a detailed representation of irrigated agriculture, and accounts for economic benefits and costs of water use. Results of this study demonstrates the capacity of the model to address emerging water-related research and practical questions.
Amy C. Green, Chris Kilsby, and András Bárdossy
Hydrol. Earth Syst. Sci., 28, 4539–4558, https://doi.org/10.5194/hess-28-4539-2024, https://doi.org/10.5194/hess-28-4539-2024, 2024
Short summary
Short summary
Weather radar is a crucial tool in rainfall estimation, but radar rainfall estimates are subject to many error sources, with the true rainfall field unknown. A flexible model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard processing methods. This flexible and efficient model performs well in generating realistic weather radar images visually for a large range of event types.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024, https://doi.org/10.5194/gmd-17-7181-2024, 2024
Short summary
Short summary
Accurate hydrologic modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall–runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Oscar M. Baez-Villanueva, Mauricio Zambrano-Bigiarini, Diego G. Miralles, Hylke E. Beck, Jonatan F. Siegmund, Camila Alvarez-Garreton, Koen Verbist, René Garreaud, Juan Pablo Boisier, and Mauricio Galleguillos
Hydrol. Earth Syst. Sci., 28, 1415–1439, https://doi.org/10.5194/hess-28-1415-2024, https://doi.org/10.5194/hess-28-1415-2024, 2024
Short summary
Short summary
Various drought indices exist, but there is no consensus on which index to use to assess streamflow droughts. This study addresses meteorological, soil moisture, and snow indices along with their temporal scales to assess streamflow drought across hydrologically diverse catchments. Using data from 100 Chilean catchments, findings suggest that there is not a single drought index that can be used for all catchments and that snow-influenced areas require drought indices with larger temporal scales.
P. Rouault, D. Courault, G. Pouget, F. Flamain, R. Lopez-Lozano, C. Doussan, M. Debolini, and M. McCabe
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 1531–1536, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1531-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1531-2023, 2023
M. G. Ziliani, M. U. Altaf, B. Aragon, R. Houborg, T. E. Franz, Y. Lu, J. Sheffield, I. Hoteit, and M. F. McCabe
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1045–1052, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1045-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1045-2022, 2022
Oscar M. Baez-Villanueva, Mauricio Zambrano-Bigiarini, Pablo A. Mendoza, Ian McNamara, Hylke E. Beck, Joschka Thurner, Alexandra Nauditt, Lars Ribbe, and Nguyen Xuan Thinh
Hydrol. Earth Syst. Sci., 25, 5805–5837, https://doi.org/10.5194/hess-25-5805-2021, https://doi.org/10.5194/hess-25-5805-2021, 2021
Short summary
Short summary
Most rivers worldwide are ungauged, which hinders the sustainable management of water resources. Regionalisation methods use information from gauged rivers to estimate streamflow over ungauged ones. Through hydrological modelling, we assessed how the selection of precipitation products affects the performance of three regionalisation methods. We found that a precipitation product that provides the best results in hydrological modelling does not necessarily perform the best for regionalisation.
Alexandra Nauditt, Kerstin Stahl, Erasmo Rodríguez, Christian Birkel, Rosa Maria Formiga-Johnsson, Kallio Marko, Hamish Hann, Lars Ribbe, Oscar M. Baez-Villanueva, and Joschka Thurner
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2020-360, https://doi.org/10.5194/nhess-2020-360, 2020
Manuscript not accepted for further review
Short summary
Short summary
Recurrent droughts are causing severe damages to tropical countries. We used gridded drought hazard and vulnerability data sets to map drought risk in four mesoscale rural tropical study regions in Latin America and Vietnam/Cambodia. Our risk maps clearly identified drought risk hotspots and displayed spatial and sector-wise distribution of hazard and vulnerability. As results were confirmed by local stakeholders our approach provides relevant information for drought managers in the Tropics.
Oliver Miguel López Valencia, Kasper Johansen, Bruno José Luis Aragón Solorio, Ting Li, Rasmus Houborg, Yoann Malbeteau, Samer AlMashharawi, Muhammad Umer Altaf, Essam Mohammed Fallatah, Hari Prasad Dasari, Ibrahim Hoteit, and Matthew Francis McCabe
Hydrol. Earth Syst. Sci., 24, 5251–5277, https://doi.org/10.5194/hess-24-5251-2020, https://doi.org/10.5194/hess-24-5251-2020, 2020
Short summary
Short summary
The agricultural sector in Saudi Arabia has expanded rapidly over the last few decades, supported by non-renewable groundwater abstraction. This study describes a novel data–model fusion approach to compile national-scale groundwater abstractions and demonstrates its use over 5000 individual center-pivot fields. This method will allow both farmers and water management agencies to make informed water accounting decisions across multiple spatial and temporal scales.
Cited articles
Abbas, A., Yang, Y., Pan, M., Tramblay, Y., Shen, C., Ji, H., Gebrechorkos, S. H., Pappenberger, F., Pyo, J. C., Feng, D., Huffman, G., Nguyen, P., Massari, C., Brocca, L., Jackson, T., and Beck, H. E.: Comprehensive Global Assessment of 23 Gridded Precipitation Datasets Across 16,295 Catchments Using Hydrological Modeling, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-4194, 2025. a, b
Adhikari, A. and Behrangi, A.: Assessment of Satellite Precipitation Products in Relation With Orographic Enhancement Over the Western United States, Earth and Space Science, 9, e2021EA001906, https://doi.org/10.1029/2021EA001906, 2022. a
Adler, R. F., Sapiano, M. R., Huffman, G. J., Wang, J.-J., Gu, G., Bolvin, D., Chiu, L., Schneider, U., Becker, A., and Nelkin, E.: The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation, Atmosphere, 9, 138, https://doi.org/10.3390/atmos9040138, 2018. a
Al-Falahi, A. H., Saddique, N., Spank, U., Gebrechorkos, S. H., and Bernhofer, C.: Evaluation the performance of several gridded precipitation products over the highland region of yemen for water resources management, Remote Sens., 12, 2984, https://doi.org/10.3390/rs12182984, 2020. a, b, c
Alharbi, R. S., Dao, V., Jimenez Arellano, C., and Nguyen, P.: Comprehensive Evaluation of Near-Real-Time Satellite-Based Precipitation: PDIR-Now over Saudi Arabia, Remote Sens., 16, 703, https://doi.org/10.3390/rs16040703, 2024. a, b, c, d
Al-Ibrahim, A. A.: Excessive use of groundwater resources in Saudi Arabia: Impacts and policy options, Ambio, 20, 34–37, http://www.jstor.org/stable/4313768 (last access: 24 September 2025), 1991. a
Almazroui, M.: Calibration of TRMM rainfall climatology over Saudi Arabia during 1998–2009, Atmos. Res., 99, 400–414, https://doi.org/10.1016/j.atmosres.2010.11.006, 2011. a
Almazroui, M.: Rainfall Trends and Extremes in Saudi Arabia in Recent Decades, Atmosphere, 11, 964, https://doi.org/10.3390/atmos11090964, 2020. a
Almazroui, M., Islam, M. N., Saeed, S., Saeed, F., and Ismail, M.: Future Changes in Climate over the Arabian Peninsula based on CMIP6 Multimodel Simulations, Earth Systems and Environment, 4, 611–630, https://doi.org/10.1007/s41748-020-00183-5, 2020. a
Al Saud, M.: Assessment of flood hazard of Jeddah area 2009, Saudi Arabia, Journal of Water Resource and Protection, 2010, https://doi.org/10.4236/jwarp.2010.29099, 2010. a
Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., van Dijk, A. I. J. M., Weedon, G. P., Brocca, L., Pappenberger, F., Huffman, G. J., and Wood, E. F.: Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling, Hydrol. Earth Syst. Sci., 21, 6201–6217, https://doi.org/10.5194/hess-21-6201-2017, 2017. a, b, c, d
Beck, H. E., Pan, M., Roy, T., Weedon, G. P., Pappenberger, F., van Dijk, A. I. J. M., Huffman, G. J., Adler, R. F., and Wood, E. F.: Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS, Hydrol. Earth Syst. Sci., 23, 207–224, https://doi.org/10.5194/hess-23-207-2019, 2019a. a
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., Dijk, A. I. J. M. V., McVicar, T. R., and Adler, R. F.: MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment, B. Am. Meteorol. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1, 2019b. a, b, c, d, e, f
Beck, H. E., Westra, S., Tan, J., Pappenberger, F., Huffman, G. J., McVicar, T. R., Gründemann, G. J., Vergopolan, N., Fowler, H. J., Lewis, E., Verbist, K., and Wood, E. F.: PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018, Scientific Data, 7, 302, https://doi.org/10.1038/s41597-020-00631-x, 2020. a
Blenkinsop, S., Fowler, H. J., Barbero, R., Chan, S. C., Guerreiro, S. B., Kendon, E., Lenderink, G., Lewis, E., Li, X.-F., Westra, S., Alexander, L., Allan, R. P., Berg, P., Dunn, R. J. H., Ekström, M., Evans, J. P., Holland, G., Jones, R., Kjellström, E., Klein-Tank, A., Lettenmaier, D., Mishra, V., Prein, A. F., Sheffield, J., and Tye, M. R.: The INTENSE project: using observations and models to understand the past, present and future of sub-daily rainfall extremes, Adv. Sci. Res., 15, 117–126, https://doi.org/10.5194/asr-15-117-2018, 2018. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., and Wagner, W.: SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations, Earth Syst. Sci. Data, 11, 1583–1601, https://doi.org/10.5194/essd-11-1583-2019, 2019. a
Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., and Wagner, W.: SM2RAIN-ASCAT (2007–2022): global daily satellite rainfall from ASCAT soil moisture (2.1.2n), Zenodo [data set], https://doi.org/10.5281/zenodo.10376109, 2023. a
Cannon, A. J., Sobie, S. R., and Murdock, T. Q.: Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes?, J. Climate, 28, 6938–6959, 2015. a
Chen, M., Shi, W., Xie, P., Silva, V. B. S., Kousky, V. E., Wayne Higgins, R., and Janowiak, J. E.: Assessing objective techniques for gauge-based analyses of global daily precipitation, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2007JD009132, 2008. a
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16, Association for Computing Machinery, New York, NY, USA, 785–794, ISBN 978-1-4503-4232-2, https://doi.org/10.1145/2939672.2939785, 2016. a
Ciabatta, L., Massari, C., Brocca, L., Gruber, A., Reimer, C., Hahn, S., Paulik, C., Dorigo, W., Kidd, R., and Wagner, W.: SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture, Earth Syst. Sci. Data, 10, 267–280, https://doi.org/10.5194/essd-10-267-2018, 2018a. a
Ciabatta, L., Massari, C., Brocca, L., Gruber, A., Reimer, C., Hahn, S., Paulik, C., Dorigo, W., Kidd, R., and Wagner, W.: SM2RAIN-CCI (1 Jan 1998 – 31 December 2015) global daily rainfall dataset (Version 2), Zenodo [data set], https://doi.org/10.5281/zenodo.1305021, 2018b. a
Ciach, G. J.: Local Random Errors in Tipping-Bucket Rain Gauge Measurements, J. Atmos. Ocean. Tech., https://doi.org/10.1175/1520-0426(2003)20<752:lreitb>2.0.co;2, 2003. a, b
Daly, C., Gibson, W. P., Taylor, G. H., Doggett, M. K., and Smith, J. I.: Observer Bias in Daily Precipitation Measurements at United States Cooperative Network Stations, B. Am. Meteorol. Soc., https://doi.org/10.1175/BAMS-88-6-899, 2007. a, b
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., Curtis, J., and Pasteris, P. P.: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States, Int. J. Climatol., 28, 2031–2064, https://doi.org/10.1002/joc.1688, 2008. a, b
Danielson, J. J. and Gesch, D. B.: Global multi-resolution terrain elevation data 2010 (GMTED2010), Tech. Rep. 2331-1258, US Geological Survey, https://doi.org/10.3133/ofr20111073, 2011. a
Derin, Y., Anagnostou, E., Berne, A., Borga, M., Boudevillain, B., Buytaert, W., Chang, C.-H., Delrieu, G., Hong, Y., Hsu, Y. C., Lavado-Casimiro, W., Manz, B., Moges, S., Nikolopoulos, E. I., Sahlu, D., Salerno, F., Rodríguez-Sánchez, J.-P., Vergara, H. J., and Yilmaz, K. K.: Multiregional Satellite Precipitation Products Evaluation over Complex Terrain, J. Hydrometeorol., https://doi.org/10.1175/JHM-D-15-0197.1, 2016. a
Dotse, S.-Q., Larbi, I., Limantol, A. M., and De Silva, L. C.: A review of the application of hybrid machine learning models to improve rainfall prediction, Modeling Earth Systems and Environment, 10, 19–44, https://doi.org/10.1007/s40808-023-01835-x, 2024. a
Ebert, E. E., Janowiak, J. E., and Kidd, C.: Comparison of Near-Real-Time Precipitation Estimates from Satellite Observations and Numerical Models, B. Am. Meteorol. Soc., https://doi.org/10.1175/BAMS-88-1-47, 2007. a, b
El Kenawy, A. M. and McCabe, M. F.: A multi-decadal assessment of the performance of gauge- and model-based rainfall products over Saudi Arabia: climatology, anomalies and trends, Int. J. Climatol., 36, 656–674, https://doi.org/10.1002/joc.4374, 2016. a, b
El Kenawy, A. M., McCabe, M. F., Lopez-Moreno, J. I., Hathal, Y., Robaa, S. M., Al Budeiri, A. L., Jadoon, K. Z., Abouelmagd, A., Eddenjal, A., Domínguez-Castro, F., Trigo, R. M., and Vicente-Serrano, S. M.: Spatial assessment of the performance of multiple high-resolution satellite-based precipitation data sets over the Middle East, Int. J. Climatol., 39, 2522–2543, https://doi.org/10.1002/joc.5968, 2019. a, b, c, d, e
Fowler, H. J., Lenderink, G., Prein, A. F., Westra, S., Allan, R. P., Ban, N., Barbero, R., Berg, P., Blenkinsop, S., Do, H. X., Guerreiro, S., Haerter, J. O., Kendon, E. J., Lewis, E., Schaer, C., Sharma, A., Villarini, G., Wasko, C., and Zhang, X.: Anthropogenic intensification of short-duration rainfall extremes, Nature Reviews Earth & Environment, 2, 107–122, https://doi.org/10.1038/s43017-020-00128-6, 2021. a
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The climate hazards infrared precipitation with stations – a new environmental record for monitoring extremes, Scientific Data, 2, 150066, https://doi.org/10.1038/sdata.2015.66, 2015. a, b, c
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, 2009. a
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset, Scientific Data, 7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020. a
Hasanean, H. and Almazroui, M.: Rainfall: Features and Variations over Saudi Arabia, A Review, Climate, 3, 578–626, https://doi.org/10.3390/cli3030578, 2015. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c
Hong, Y., Hsu, K.-L., Sorooshian, S., and Gao, X.: Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System, J. Appl. Meteorol. Clim., 43, 1834–1853, https://doi.org/10.1175/JAM2173.1, 2004. a
Hrachowitz, M., Savenije, H., Blöschl, G., McDonnell, J. J., Sivapalan, M., Pomeroy, J., Arheimer, B., Blume, T., Clark, M., and Ehret, U.: A decade of Predictions in Ungauged Basins (PUB) – a review, Hydrolog. Sci. J., 58, 1198–1255, 2013. a
Huffman, G. J.: The transition in multi-satellite products from TRMM to GPM (TMPA to IMERG), Algorithm Information Document, 2019. a
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales, J. Hydrometeorol., https://doi.org/10.1175/JHM560.1, 2007. a
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Tan, J.: GPM IMERG final precipitation L3 half hourly 0.1 degree x 0.1 degree V07, Goddard Earth Sciences Data and Information Services Center (GES DISC), Greenbelt, MD, USA, https://doi.org/10.5067/GPM/IMERG/3B-HH/07, 2019. a, b, c
Huffman, G. J., Adler, R. F., Behrangi, A., Bolvin, D. T., Nelkin, E. J., Gu, G., and Ehsani, M. R.: The new version 3.2 global precipitation climatology project (GPCP) monthly and daily precipitation products, J. Climate, 36, 7635–7655, 2023. a
Hussein, E. A., Ghaziasgar, M., Thron, C., Vaccari, M., and Jafta, Y.: Rainfall prediction using machine learning models: literature survey, Artificial Intelligence for Data Science in Theory and Practice, Springer, 75–108, https://doi.org/10.1007/978-3-030-92245-0_4, 2022. a
Intergovernmental Panel on Climate Change (IPCC): Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, https://doi.org/10.1017/9781009157896, 2023. a
Iturbide, M., Fernández, J., Gutiérrez, J. M., Bedia, J., Cimadevilla, E., Díez-Sierra, J., Manzanas, R., Casanueva, A., Baño-Medina, J., and Milovac, J.: Implementation of fair principles in the IPCC: the WGI ar6 atlas repository, Scientific Data, 9, https://doi.org/10.1038/s41597-022-01739-y, 2022. a
Jazem Ghanim, A. A., Anjum, M. N., Alharbi, R. S., Aurangzaib, M., Zafar, U., Rehamn, A., Irfan, M., Rahman, S., Faraj Mursal, S. N., and Alyami, S.: Spatiotemporal evaluation of five satellite-based precipitation products under the arid environment of Saudi Arabia, AIP Advances, 14, https://doi.org/10.1063/5.0191924, 2024. a, b, c, d, e
Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P.: CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution, J. Hydrometeorol., 5, 487–503, https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2, 2004. a
Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., and Kessler, M.: Climatologies at high resolution for the earth's land surface areas, Scientific Data, 4, 170122, https://doi.org/10.1038/sdata.2017.122, 2017. a, b
Kidd, C., Becker, A., Huffman, G. J., Muller, C. L., Joe, P., Skofronick-Jackson, G., and Kirschbaum, D. B.: So, How Much of the Earth's Surface Is Covered by Rain Gauges?, B. Am. Meteorol. Soc., https://doi.org/10.1175/BAMS-D-14-00283.1, 2017. a, b
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424, 264–277, 2012. a
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019. a
Kochendorfer, J., Rasmussen, R., Wolff, M., Baker, B., Hall, M. E., Meyers, T., Landolt, S., Jachcik, A., Isaksen, K., Brækkan, R., and Leeper, R.: The quantification and correction of wind-induced precipitation measurement errors, Hydrol. Earth Syst. Sci., 21, 1973–1989, https://doi.org/10.5194/hess-21-1973-2017, 2017. a
Kosaka, Y., Kobayashi, S., Harada, Y., Kobayashi, C., Naoe, H., Yoshimoto, K., Harada, M., Goto, N., Chiba, J., Miyaoka, K., Sekiguchi, R., Deushi, M., Kamahori, H., Nakaegawa, T., Tanaka, T., Tokuhiro, T., Sato, Y., Matsushita, Y., and Onogi, K.: The JRA-3Q Reanalysis, J. Meteorol. Soc. Jpn. Ser. II, 102, https://doi.org/10.2151/jmsj.2024-004, 2024. a, b
Kubota, T., Aonashi, K., Ushio, T., Shige, S., Takayabu, Y. N., Kachi, M., Arai, Y., Tashima, T., Masaki, T., and Kawamoto, N.: Global Satellite Mapping of Precipitation (GSMaP) products in the GPM era, Satellite Precipitation Measurement, Springer, vol. 1, 355–373, https://doi.org/10.1007/978-3-030-24568-9_20, 2020. a, b
Lewis, E., Fowler, H., Alexander, L., Dunn, R., McClean, F., Barbero, R., Guerreiro, S., Li, X.-F., and Blenkinsop, S.: GSDR: A Global Sub-Daily Rainfall Dataset, J. Climate, 32, 4715–4729, https://doi.org/10.1175/JCLI-D-18-0143.1, 2019. a
Lin, J., Qian, T., Bechtold, P., Grell, G., Zhang, G. J., Zhu, P., Freitas, S. R., Barnes, H., and Han, J.: Atmospheric Convection, Atmos.-Ocean, 60, 422–476, https://doi.org/10.1080/07055900.2022.2082915, 2022. a
Lin, Y. and Mitchell, K. E.: The NCEP stage II/IV hourly precipitation analyses: Development and applications, in: 19th Conference Hydrology, American Meteorological Society, San Diego, CA, USA, vol. 10, Citeseer, https://ams.confex.com/ams/pdfpapers/83847.pdf (last access: 18 September 2025), 2005. a, b
Mahmoud, M. T., Al-Zahrani, M. A., and Sharif, H. O.: Assessment of global precipitation measurement satellite products over Saudi Arabia, J. Hydrol., 559, 1–12, https://doi.org/10.1016/j.jhydrol.2018.02.015, 2018. a, b, c
Massari, C.: GPM+SM2RAIN (2007–2018): quasi-global 25km/daily rainfall product from the integration of GPM and SM2RAIN-based rainfall products (0.1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.3854817, 2020. a
Massari, C., Brocca, L., Pellarin, T., Abramowitz, G., Filippucci, P., Ciabatta, L., Maggioni, V., Kerr, Y., and Fernandez Prieto, D.: A daily 25 km short-latency rainfall product for data-scarce regions based on the integration of the Global Precipitation Measurement mission rainfall and multiple-satellite soil moisture products, Hydrol. Earth Syst. Sci., 24, 2687–2710, https://doi.org/10.5194/hess-24-2687-2020, 2020. a, b, c
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An Overview of the Global Historical Climatology Network-Daily Database, J. Atmos. Ocean. Tech., 29, 897–910, https://doi.org/10.1175/JTECH-D-11-00103.1, 2012. a, b, c
Ministry of Environment, Water and Agriculture: National Water Strategy, Ministry of Environment, Water and Agriculture, https://www.mewa.gov.sa/en/Ministry/Agencies/TheWaterAgency/Topics/Pages/Strategy.aspx, last access: 25 September 2025. a
Munir, S., Kamil, S., Habeebullah, T. M., Zaidi, S., Alhajji, Z., and Siddiqui, M. H.: Drought Variabilities in Saudi Arabia: Unveiling Spatiotemporal Trends through Observations and Projections, Earth Systems and Environment, 9, 563–587, https://doi.org/10.1007/s41748-025-00570-w, 2025. a
New, M., Hulme, M., and Jones, P.: Representing Twentieth-Century Space–Time Climate Variability. Part I: Development of a 1961–90 Mean Monthly Terrestrial Climatology, J. Climate, 12, 829–856, https://doi.org/https://doi.org/10.1175/1520-0442(2000)013<2217:RTCSTC>2.0.CO;2, 1999. a
Nguyen, P., Ombadi, M., Sorooshian, S., Hsu, K., AghaKouchak, A., Braithwaite, D., Ashouri, H., and Thorstensen, A. R.: The PERSIANN family of global satellite precipitation data: a review and evaluation of products, Hydrol. Earth Syst. Sci., 22, 5801–5816, https://doi.org/10.5194/hess-22-5801-2018, 2018. a
Nguyen, P., Ombadi, M., Gorooh, V. A., Shearer, E. J., Sadeghi, M., Sorooshian, S., Hsu, K., Bolvin, D., and Ralph, M. F.: PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset, J. Hydrometeorol., 21, 2893–2906, https://doi.org/10.1175/JHM-D-20-0177.1, 2020. a
O, S. and Foelsche, U.: Assessment of spatial uncertainty of heavy rainfall at catchment scale using a dense gauge network, Hydrol. Earth Syst. Sci., 23, 2863–2875, https://doi.org/10.5194/hess-23-2863-2019, 2019. a
Osborn, T. and Hulme, M.: Development of a relationship between station and grid-box rainday frequencies for climate model evaluation, J. Climate, 10, 1885–1908, 1997. a
Othman, A., El-Saoud, W. A., Habeebullah, T., Shaaban, F., and Abotalib, A. Z.: Risk assessment of flash flood and soil erosion impacts on electrical infrastructures in overcrowded mountainous urban areas under climate change, Reliab. Eng. Syst. Safe., 236, 109302, https://doi.org/10.1016/j.ress.2023.109302, 2023. a
Overeem, A., van den Besselaar, E., van der Schrier, G., Meirink, J. F., van der Plas, E., and Leijnse, H.: EURADCLIM: the European climatological high-resolution gauge-adjusted radar precipitation dataset, Earth Syst. Sci. Data, 15, 1441–1464, https://doi.org/10.5194/essd-15-1441-2023, 2023. a, b
Papacharalampous, G., Tyralis, H., Doulamis, A., and Doulamis, N.: Comparison of Machine Learning Algorithms for Merging Gridded Satellite and Earth-Observed Precipitation Data, Water, 15, 634, https://doi.org/10.3390/w15040634, 2023. a
Patlakas, P., Stathopoulos, C., Flocas, H., Bartsotas, N. S., and Kallos, G.: Precipitation Climatology for the Arid Region of the Arabian Peninsula – Variability, Trends and Extremes, Climate, 9, 103, https://doi.org/10.3390/cli9070103, 2021. a
Peters, K., Hohenegger, C., and Klocke, D.: Different Representation of Mesoscale Convective Systems in Convection-Permitting and Convection-Parameterizing NWP Models and Its Implications for Large-Scale Forecast Evolution, Atmosphere, 10, 503, https://doi.org/10.3390/atmos10090503, 2019. a
Prakash, S.: Performance assessment of CHIRPS, MSWEP, SM2RAIN-CCI, and TMPA precipitation products across India, J. Hydrol., 571, 50–59, https://doi.org/10.1016/j.jhydrol.2019.01.036, 2019. a
Sadeghi, M., Nguyen, P., Naeini, M. R., Hsu, K., Braithwaite, D., and Sorooshian, S.: PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies, Scientific Data, 8, 157, https://doi.org/10.1038/s41597-021-00940-9, 2021. a
Sevruk, B., Ondrás, M., and Chvíla, B.: The WMO precipitation measurement intercomparisons, Atmos. Res., 92, 376–380, https://doi.org/10.1016/j.atmosres.2009.01.016, 2009. a, b
Shen, Z., Yong, B., Gourley, J. J., Qi, W., Lu, D., Liu, J., Ren, L., Hong, Y., and Zhang, J.: Recent global performance of the Climate Hazards group Infrared Precipitation (CHIRP) with Stations (CHIRPS), J. Hydrol., 591, 125284, https://doi.org/10.1016/j.jhydrol.2020.125284, 2020. a
Sivapalan, M., Takeuchi, K., Franks, S., Gupta, V., Karambiri, H., Lakshmi, V., Liang, X., McDonnell, J., Mendiondo, E. M., and O'connell, P.: IAHS Decade on Predictions in Ungauged Basins (PUB), 2003–2012: Shaping an exciting future for the hydrological sciences, Hydrolog. Sci. J., 48, 857–880, 2003. a
Smith, A., Lott, N., and Vose, R.: The Integrated Surface Database: Recent Developments and Partnerships, B. Am. Meteorol. Soc., https://doi.org/10.1175/2011BAMS3015.1, 2011. a
Sultan, M., Sturchio, N. C., Alsefry, S., Emil, M. K., Ahmed, M., Abdelmohsen, K., AbuAbdullah, M. M., Yan, E., Save, H., Alharbi, T., Othman, A., and Chouinard, K.: Assessment of age, origin, and sustainability of fossil aquifers: A geochemical and remote sensing-based approach, J. Hydrol., 576, 325–341, https://doi.org/10.1016/j.jhydrol.2019.06.017, 2019. a
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K.-L.: A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons, Rev. Geophys., 56, 79–107, https://doi.org/10.1002/2017RG000574, 2018. a, b, c
Tabari, H. and Willems, P.: Seasonally varying footprint of climate change on precipitation in the Middle East, Sci. Rep., 8, 4435, https://doi.org/10.1038/s41598-018-22795-8, 2018. a
Tang, G., Behrangi, A., Long, D., Li, C., and Hong, Y.: Accounting for spatiotemporal errors of gauges: A critical step to evaluate gridded precipitation products, J. Hydrol., 559, 294–306, https://doi.org/10.1016/j.jhydrol.2018.02.057, 2018. a
Ting, Y.-S.: Why Machine Learning Models Systematically Underestimate Extreme Values, arXiv [preprint], https://doi.org/10.48550/arXiv.2412.05806, 15 July 2025. a, b
Trabucco, A. and Zomer, R. J.: Global aridity index and potential evapotranspiration (ET0) climate database v2, CGIAR Consort. Spat. Inf., 10, m9, https://doi.org/10.6084/m9.figshare.7504448.v3, 2018. a
Villarini, G., Mandapaka, P. V., Krajewski, W. F., and Moore, R. J.: Rainfall and sampling uncertainties: A rain gauge perspective, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2007JD009214, 2008. a
Wang, Y., You, Y., and Kulie, M.: Global Virga Precipitation Distribution Derived From Three Spaceborne Radars and Its Contribution to the False Radiometer Precipitation Detection, Geophys. Res. Lett., 45, 4446–4455, https://doi.org/10.1029/2018GL077891, 2018. a
Xie, P., Joyce, R., Wu, S., Yoo, S.-H., Yarosh, Y., Sun, F., and Lin, R.: Reprocessed, Bias-Corrected CMORPH Global High-Resolution Precipitation Estimates from 1998, J. Hydrometeorol., 18, 1617–1641, https://doi.org/10.1175/JHM-D-16-0168.1, 2017. a
Xu, Y., Tang, G., Li, L., and Wan, W.: Multi-source precipitation estimation using machine learning: Clarification and benchmarking, J. Hydrol., 635, 131195, https://doi.org/10.1016/j.jhydrol.2024.131195, 2024. a, b
Yamamoto, M. K., Shige, S., Yu, C.-K., and Cheng, L.-W.: Further Improvement of the Heavy Orographic Rainfall Retrievals in the GSMaP Algorithm for Microwave Radiometers, J. Appl. Meteorol. Clim., 56, 2607–2619, https://doi.org/10.1175/JAMC-D-16-0332.1, 2017. a
Yang, S., Jones, P. D., Jiang, H., and Zhou, Z.: Development of a near-real-time global in situ daily precipitation dataset for 0000–0000 UTC, Int. J. Climatol., 40, 2795–2810, https://doi.org/10.1002/joc.6367, 2020. a, b, c
Yano, J.-I., Ziemiański, M. Z., Cullen, M., Termonia, P., Onvlee, J., Bengtsson, L., Carrassi, A., Davy, R., Deluca, A., Gray, S. L., Homar, V., Köhler, M., Krichak, S., Michaelides, S., Phillips, V. T. J., Soares, P. M. M., and Wyszogrodzki, A. A.: Scientific Challenges of Convective-Scale Numerical Weather Prediction, B. Am. Meteorol. Soc., https://doi.org/10.1175/BAMS-D-17-0125.1, 2018. a
Yates, E., Anquetin, S., Ducrocq, V., Creutin, J.-D., Ricard, D., and Chancibault, K.: Point and areal validation of forecast precipitation fields, Meteorol. Appl., 13, 1–20, https://doi.org/10.1017/S1350482705001921, 2006. a, b, c
Youssef, A. M., Sefry, S. A., Pradhan, B., and Alfadail, E. A.: Analysis on causes of flash flood in Jeddah city (Kingdom of Saudi Arabia) of 2009 and 2011 using multi-sensor remote sensing data and GIS, Geomat. Nat. Haz. Risk, 7, 1018–1042, https://doi.org/10.1080/19475705.2015.1012750, 2016. a
Short summary
Our paper introduces Saudi Rainfall (SaRa), a high-resolution, near-real-time rainfall product for the Arabian Peninsula. Using machine learning, SaRa combines multiple satellite and (re)analysis datasets with static predictors, outperforming existing products in the region. With the fast development and continuing growth in water demand over this region, SaRa could help to address water challenges and support resource management.
Our paper introduces Saudi Rainfall (SaRa), a high-resolution, near-real-time rainfall product...
