Collaboration across modeling, observational, and process research communities aims to improve how models represent and predict precipitation.

Many extreme events and related impacts are associated with the intensity, duration, and frequency of precipitation, including drought, flooding, wildfire, and severe storms. Understanding when, where, and how much precipitation will fall can help decision-makers and planners in agriculture, emergency management, energy, and other sectors prepare for and reduce costs from potential impacts. While models are skilled at simulating global and regional temperature, precipitation-related processes are not captured as well, and producing accurate forecasts and realistic projections with enough lead time to support decision-making is an ongoing science challenge.

Recognizing the need for improved skill in precipitation prediction, NOAA and DOE led a workshop in November–December 2020 that focused on advancing understanding of precipitation predictability and physical processes key to precipitation biases (or systematic model errors). The workshop brought together the observational, modeling, and research communities to identify sources of predictability that span weather to climate time scales, identify which physical processes could be better represented in models to improve the accuracy of future predictions, evaluate which existing and desired observing systems are needed to address shortcomings, and suggest which Federal and international collaborations would be appropriate to accelerate progress.