Weather extremes in a changing climate are of vital socioeconomic interest to the world and substantially impact agriculture, energy, infrastructure, human health, and commodities trading. Consequently, there is significant interdisciplinary incentive to understand the extent to which these extreme events can be predicted in advance on a global scale. In particular, improving forecasts of precipitation at Subseasonal-to-Seasonal (S2S; 2-week to 6-month) lead times is of particular interest for many stakeholders around the globe.

I am currently the Subseasonal and Seasonal Team Lead Researcher at the Center for Western Weather and Water Extremes (CW3E) at Scripps Institution of Oceanography (SIO), University of California, San Diego. Our team's efforts are described here on the CW3E website, and are primarily supported by the California Department of Water Resources and the United States Army Corps of Engineers.

Our team is highly interactive, and we foster a collaborative environment to tackle the many challenging issues involved in subseasonal and seasonal prediction. We use decades of operational dynamical model data, as well as statistical and machine learning models, in an effort to quantify predictability limits and prediction skill of atmospheric rivers (ARs) and precipitation over the western United States. ARs are of particular interest in this region, since 84% of flood damages over the last 40 years have been caused by ARs (Corringham et al. 2019).