Storylines are a promising approach for understanding how specific sequences of climate-related events may play out over time. Storyline approaches are well-suited to examine dynamical interactions among the processes that contribute to hydrometeorological extremes and their impacts on human systems. This approach has also proven useful for practitioners as it allows them to do a deep-dive into impactful extreme events that caused system stress in the past (as well as hypothetical variants of those events), and examine how such events might impact their future vulnerabilities and adaptation options. Given that our understanding of future climate changes are determined in part by deeply uncertain factors such as greenhouse gas emission pathways, and structural biases that influence model responses, storylines offer a tractable way of examining plausible future conditions. However, there are cases where knowing the probability (not just the plausibility) of such storylines is also critical, e.g. in situations where preparing for certain storylines would be substantially more costly than others. Extreme value theory offers a set of statistical tools for characterizing the probability of extreme events arising from internal climate variability, but cannot fully eliminate deep uncertainty. Moreover, our ability to characterize internal variability for extreme value analysis is limited by the observational record and by computational constraints on the number of climate model ensemble runs available at sufficiently high resolution to capture relevant extremes. Reconciling the deeply uncertain aspects of future climate change with those aspects that are amenable to probabilistic characterization is thus a key challenge for providing decision-relevant information about extreme event storylines and their likelihoods. Here we describe conceptual frameworks and methodologies being co-produced by scientists and resource managers within the HyperFACETS project to advance probabilistic storylines. We demonstrate the approach by examining how climate change could alter both the character and frequency of the historic 1997 flood event in California, which was driven by the interaction of an extreme atmospheric river with pre-existing snowpack and saturated soils that led to widespread flood inundation.