Wang, Jingyu https://orcid.org/0000-0002-4841-0872
Wang, Xianfeng https://orcid.org/0000-0002-8614-5627
Sun, Shuyu https://orcid.org/0000-0002-3078-864X
Wen, Yonggang https://orcid.org/0000-0002-2751-5114
Pathak, Raju https://orcid.org/0000-0002-2734-4300
Dong, Luojie https://orcid.org/0009-0007-7911-6012
Park, Edward https://orcid.org/0000-0002-1299-1724
Hoteit, Ibrahim https://orcid.org/0000-0002-3751-4393
Funding for this research was provided by:
Agency for Science, Technology and Research (Cognitive Digital Twin and Physics-based ML for Ba)
National Key Research and Development Project of China (2023YFA1011701)
National Natural Science Foundation of China (12571466)
Ministry of Education of Singapore (MOE-T2EP50124-0018)
National Research Foundation, Prime Minister’s Office, Singapore, and National University of Singapore (Sustainable Tropical Data Centre Test bed program)
Article Title: Key drivers and predictability of the unprecedented 2024 United Arab Emirates flood
Journal Title: Environmental Research Letters
Article Type: paper
Copyright Information: © 2026 The Author(s). Published by IOP Publishing Ltd
Publication dates
Date Received: 2025-09-13
Date Accepted: 2025-12-23
Online publication date: 2026-01-06