- 1Newcastle University, School of Engineering, United Kingdom (k.zhu8@newcastle.ac.uk)
- 2Institute of Energy, Environment and Economy, Tsinghua University, Beijing 100084, China
- 3Geographical Science, Beijing Normal University, Beijing, China (yuanyuan.bai@mail.bnu.edu.cn)
- 4Department of Land Surveying and Geo-Informatics and Research Institute for Land and Space, The Hong Kong Polytechnic University, Hong Kong, China (lsgi-mo.zhou@connect.polyu.hk)
- 5State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster, Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Informatio
- 6School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing, China (dongyuchen0212@163.com)
- 7Institute of Urban Meteorology, China Meteorological Administration, Beijing, China (lpei@ium.cn)
- 8School of Atmospheric Sciences, Nanjing University, Nanjing, China (wenxiazhang@nju.edu.cn)
- 9Department of Meteorology, University of Reading, United Kingdom (b.dong@reading.ac.uk)
- 10School of Geosciences, University of Edinburgh, United Kingdom (Simon.Tett@ed.ac.uk)
- 11School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China (liqingx5@mail.sysu.edu.cn)
How to cite: Zhu, K., Carruthers, J., Bai, Y., Zhou, M., Dong, Y., Pei, L., Zhang, W., Dong, B., Tett, S., and Li, Q.: AI-assisted projection and impact assessment of high-impact extreme rainfall: physical insights from the July 2021 Zhengzhou event, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-431, https://doi.org/10.5194/ems2026-431, 2026.