Amplifying exploration of regional climate risks: clustering future projections on regionally relevant impact drivers instead of emission scenarios

Abstract Climate impacts will continue to evolve over the coming decades, requiring regions worldwide to obtain actionable climate information. Global climate models (GCMs) are often used to explore future conditions, but the variability of projections among GCMs complicates regional climate risk assessments. This variability in future projections is only partly explained by the often-used emission scenarios. Model uncertainty and internal variability play a major role in the outcomes of projected meteorological conditions, especially for local precipitation patterns. As precipitation is a key driver for hazards such as floods, droughts, and wildfires, local assessment of resulting risks using emission-based multi-model means probably leads to limited impact exploration. This study proposes a method to select more impact-relevant scenarios by determining regionally relevant climatic impact drivers and clustering GCMs on their projected changes in these drivers. We quantify the effectiveness of our approach by comparing future impacts covered by multi-model means per emission scenario with our approach, expressed as an ‘exploratory amplification’ factor. We illustrate the method for flood risk in the Latvian Lielupe basin and find the novel method has an exploratory amplification up to a factor of eight for the mid-century. We conclude that our method results in locally relevant climate scenarios that significantly improve regional exploration of future climate impacts. Such scenarios provide targeted risk information that can be used in adaptation planning.