Decision-Relevant Climate Storylines: Using Seasonal Decision-Scaling to Identify Flood Changes in Uncertain GCM Trends

As global climate patterns shift, Europe faces increasing challenges from key risks such as floods. However, translating this knowledge into locally usable risk information presents a significant challenge. A primary reason is the large variability associated with climate projection outcomes, particularly in precipitation patterns. This paper introduces a seasonal decision-scaling approach to identify decision-relevant climate storylines for regional discharge patterns, which are crucial in assessing flood risks. We sample scenarios within the uncertainty space of future projections and employ a statistical weather generator to determine probabilistic flow changes. Through the analysis of flow changes across various climate scenarios, we identify the most impactful seasonal climate parameters. These parameters are then used to cluster Global Climate Models, from which we create a set of decision-relevant climatological storylines for floods. A case study in Latvia demonstrates that river flows depend on only a few key seasonal parameters, indicating that the uncertainty can be effectively captured with a select number of distinct climatological storylines. This study not only simplifies the complexity of analysing future climate risks but also enhances the practicality of climate information at the regional level. Our novel seasonal approach to decision-scaling and the selection of decision-relevant climate storylines can be applied globally in areas where GCMs indicate varying climate trends and can also be used for drought analyses. This methodology leads to simpler climate risk information, thereby fostering improved and more robust adaptation strategies.