This session is a follow-on to last year's session co-hosted by the AI committee. This year, we invite submissions on topics related to machine learning and statistical methods applied to hydrologic problems, with a focus on real-world forecasting applications, and in particular the problem of quantitative precipitation estimation in real-time. Overlapping closely with this year's annual meeting theme "Observations Lead the Way," the QPE problem includes all observational methods: in-situ, radar-derived, satellite-derived, multi-sensor; together with objective analysis approaches. In-short -- what combinations of observations and classes of observations seem to "do best;" how do we determine what "doing best" really is (space and time scales); and how can and do AI methods (machine learning, optimal statistics, etc.) applied to the observations improve our QPE estimates? What challenges still remain? For additional information, please contact the program organizers, Valliappa Lakshmanan (firstname.lastname@example.org) or John McHenry (email@example.com).