For more than 75 years, high-hazard structures in the U.S., including dams and nuclear power plants, have been engineered to withstand floods resulting from the most unlikely but possible precipitation, termed Probable Maximum Precipitation (PMP). Estimates of PMP have relied on the fundamental physical processes that occur in extreme rainstorms, along with subjective decisions regarding storm transposition – in other words, where an observed extreme storm might plausibly be able to also occur. The 2024 National Academies report “Modernizing Probable Maximum Precipitation Estimation” provided a critical assessment of the scientific basis for PMP, including how improved meteorological understanding has, and has not, enabled improvements in estimating PMP.

Important advances in understanding extreme rainstorms have emerged in recent decades, enabled by field campaigns, radar observations, and improved numerical and conceptual models. For example, the details of rainfall production in atmospheric rivers, mesoscale convective systems, and tropical cyclones have been thoroughly investigated and quantified. On the other hand, there remain key gaps in knowledge, including how to distinguish the characteristics of rainstorms capable of approaching PMP from those producing less-extreme amounts of rain, whether numerical models can adequately simulate the most-extreme events, and how climate change is altering the characteristics of extreme rainstorms.

One key conclusion of the study is, “Major scientific advances have been made in understanding extreme rainfall since the 1994 National Research Council study of PMP, but they have not translated to major advances in methods for estimating PMP.” This presentation will feature findings from the 2024 report to discuss why this is the case, including continued limitations in observations, modeling capabilities, fundamental understanding, and institutional factors. The 2024 report also provides a vision and roadmap for advancing the science of the most extreme precipitation, and points toward how these advances will improve PMP estimation.