Modeling has been used to characterize and map natural hazards and hazard susceptibility for decades. Uncertainties are pervasive in natural hazards analysis, including a limited ability to predict where and when extreme events will occur, with what consequences, and driven by what contributing factors. Modeling efforts are challenged by the intrinsic variability of natural and human systems, missing or erroneous data, parametric uncertainty, model‐based or structural uncertainty, and knowledge gaps, among other factors. Further, scientists and engineers must translate these uncertainties to inform policy decision making, which entails its own set of uncertainties regarding valuation, understanding limitations, societal preferences, and cost‐benefit analysis. Thus, it is crucial to develop robust and meaningful approaches to characterize and communicate uncertainties.