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Throughout history, Europe and North America have experienced intense and long-lasting droughts with large impacts on society and ecosystem such as the recent drought 2018/2019 and historical drought 1540 in Europe, or the US Dust Bowl of the 1930s. To increase resilience and develop adaptation strategies to such extreme droughts, it is important to understand how dry a worst-case drought would be and how long it would take to recover from it. This study introduces and evaluates a methodological framework to generate coherent climate model-based drought storylines of different severities and for different locations. The so-called iterative ensemble resampling method repeatedly runs large ensembles and only keeps those ensemble members, which minimize local precipitation. The drought storylines are developed with the fully coupled global climate model CESM1. The first part of the analysis demonstrates the feasibility of the framework by generating some of the most extreme droughts possible. Using stringent precipitation criteria, accumulated precipitation is reduced by 80% relative to the long-term average in western Europe and by 77% in central North America, respectively, over multiple years. The number of dry days in the Western European storyline corresponds to estimates in the reconstructed drought 1540 in Central Europe. The low precipitation induces soil moisture deficit storylines that are physically consistent but beyond high return levels estimated based on purely statistically fitted generalized extreme value (GEV) distributions. In the second part, the drought storylines are used as a setup to assess the recovery time of such extreme soil moisture deficits. Over the driest regions in central and western Europe as well as central and eastern North America, the soil moisture recovers over a period of a few months up to more than five years, depending on the mean atmospheric circulation rather than on the strength of the soil moisture deficit. The framework of iterative ensemble resampling can generate up to very rare physically consistent storylines to conduct idealized experiments. When lowering the selection criteria for precipitation, the framework can be used to generate less extreme drought storylines that are more likely to occur in the real world. This approach can help to stress test the socio-economic system and adaptation strategies for potential long-lasting drought periods.

Abstract Seasonal prediction is one important element in a seamless prediction chain between weather forecasts and climate projections. After several years of development in collaboration with Universität Hamburg and Max Planck Institute for Meteorology, the Deutscher Wetterdienst performs operational seasonal forecasts since 2016 with the German Climate Forecast System, now in Version 2 (GCFS2.0). Here, the configuration of the previous system GCFS1.0 and the current GCFS2.0 are described and the performance of the two systems is compared over the common hindcast period of 1990–2014. In GCFS2.0, the forecast skill is improved compared to GCFS1.0 during boreal winter, especially for the Northern Hemisphere where the Pearson correlation has increased for the North Atlantic Oscillation index. Overall, a similar performance of GCFS2.0 in comparison to GCFS1.0 is assessed during the boreal summer. Future developments for climate forecasts need a stronger focus on the performance of interannual variability in a model system.