Primary Convener:
Xiaodong Chen, University of Oklahoma

Convener:
Tiantian Yang, University of Oklahoma Norman Campus
Ming Pan, Center for Western Weather and Water Extremes (CW3E), Scripps Institution of Oceanography, University of California San Diego
Kelly Mahoney, Cooperative Institute for Research in Environmental Sciences
Nana Liu, Texas A & M University Corpus Christi

Early Career Convener:
Nana Liu, CIWRO/OU, NOAA/NSSL

Chair:
Xiaodong Chen, University of Washington Seattle Campus
Ming Pan, Center for Western Weather and Water Extremes (CW3E), Scripps Institution of Oceanography, University of California San Diego
Tiantian Yang, University of Oklahoma Norman Campus
Nana Liu, Texas A & M University Corpus Christi

Accurately predicting precipitation across multiple time scales—ranging from short-term to subseasonal-to-seasonal, decadal, and climate scales—is essential for advancing our understanding of the water cycle and supporting sustainable water resource management. Reliable spatial and temporal analyses of precipitation patterns and mechanisms can benefit critical sectors such as energy, transportation, agriculture, and disaster preparedness. Artificial intelligence (AI)–based weather prediction is rapidly emerging alongside traditional numerical weather and climate models, offering new opportunities for innovation. This session highlights interdisciplinary research that integrates physical modeling and AI to deepen our understanding of precipitation and expand its practical applications. We welcome abstracts that explore, but are not limited to: AI and physical model–based precipitation prediction and analysis; the use of diverse observational data and AI to assess precipitation predictability; and the challenges and opportunities of forecasting precipitation in the era of AI. Contributions from atmospheric and hydrological sciences, and data/AI science are especially encouraged.

Index Terms
3354 Precipitation
1655 Water cycles
1854 Precipitation
1942 Machine learning

Suggested Itineraries:
Disasters‚ Calamities and Extreme Events
Climate Change and Global Policy
Machine Learning and AI
Open Science and Open Data

Cross-Listed:
NH - Natural Hazards
H - Hydrology
GC - Global Environmental Change

Neighborhoods:
3. Earth Covering