Varying sources of uncertainty in risk-relevant hazard projections

A growing number of societal actors rely on high-resolution meteorological information to understand a changing landscape of physical hazards. Within this context, accounting for uncertainty is crucial to quantify and manage risks, but can be challenging given the potential for various sources of uncertainty to manifest differently across use-cases. Here, we combine three state-of-the-art downscaled ensembles to characterize how different uncertainties affect projections of several temperature- and precipitation-based risk metrics across the contiguous United States. We focus on long-term trends of aggregate indices as well as the intensity of rare events with 10- to 100-year return periods. By leveraging new downscaled initial condition ensembles, we characterize the role of internal variability at local scales and estimate its importance relative to other sources of uncertainty. Our results demonstrate systematic differences in patterns of uncertainty between average and extreme indices, across recurrence intervals, and between temperature- and precipitation-derived variables. We show that temperature metrics are more sensitive to the choice of radiative forcing scenario and Earth system model, while internal variability is often dominant for precipitation-based metrics. Additionally, we find that the statistical uncertainty from extreme value distribution fitting can often exceed the uncertainties related to Earth system modeling, particularly at recurrence intervals of 50 years or longer. Our results can provide guidance for researchers and practitioners conducting physical hazard risk assessment.