Large-scale dynamical and thermodynamical processes are common environmental drivers of extreme weather events. However, such large-scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating extreme weather events and associated risks in current and future climate. In this paper, a machine learning (ML) approach was employed to bias correct the large-scale wind, temperature, and humidity simulated by the E3SM atmosphere model at $\sim 1^\circ$ resolution. The usefulness of the proposed ML approach for extreme weather analysis was demonstrated with a focus on three extreme weather events, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large-scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve the water vapor transport associated with ARs, and the representations of thermodynamical flows associated with ETCs. When the bias-corrected large-scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large-scale storm environments as well as the occurrence and intensity of three extreme events. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large-scale storm environments simulated by low-resolution climate models.

Hydropower is a low-carbon emission renewable energy source that provides competitive and flexible electricity generation and is essential to the evolving power grid in the context of decarbonization. Assessing hydropower availability in a changing climate is technically challenging because there is a lack of consensus in the modeling representation of key dynamics across scales and processes. The SECURE Water Act requires a periodic assessment of the impact of climate change on the United States federal hydropower. The uncertainties associated with the structure of the tools in the previous assessment was limited to an ensemble of climate models. We leverage the second assessment to evaluate the compounded impact of climate and reservoir-hydropower models’ structural uncertainties on monthly hydropower projections. While the second assessment relies on a mostly-statistical regression-based hydropower model, we introduce a mostly-conceptual reservoir operations-hydropower model. Using two different types of hydropower model allows us to provide the first hydropower assessment with uncertainty partitioning associated with both climate and hydropower models. We also update the second assessment, performed initially at an annual time scale, to a seasonal time scale. Results suggest that at least 50% of the uncertainties, both at annual and seasonal scales, are attributed to the climate models. The annual predictions are consistent between hydropower models which marginally contribute to the variability in annual projections. However, up to 50% of seasonal variability can be attributed to the choice of the hydropower model in regions over the western US where the reservoir storage is substantial. The analysis identifies regions where multi-model assessments are needed and presents a novel approach to partition uncertainties in hydropower projections. Another outcome includes an updated evaluation of CMIP5-based federal hydropower projection, at the monthly scale and with a larger ensemble, which can provide a baseline for understanding the upcoming 3rd assessment based on CMIP6 projections.