The increasing frequency and intensity of extreme weather events, such as heatwaves, droughts, and extreme rainfall, highlight the urgent need to understand the role of climate change. Storm Boris, which brought unprecedented rainfall to Central and Eastern Europe in September 2024, caused significant damage and loss of life, prompting questions about the contribution of climate change to the event's severity and future projections. Probabilistic methods, widely used in climate change attribution studies, rely on observational data and large climate model ensembles. However, these methods face challenges with record-breaking events, potential model discrepancies, and communication to non-scientists. Storyline simulations offer a complementary approach, providing relatable "what-if" scenarios by comparing real-world events under different climate conditions (pre-industrial, present-day, and future warmer climates). This study leverages this approach to investigate storm Boris, aiming to provide a more accessible and impactful understanding of climate change's influence on extreme rainfall.

Existing research predominantly utilizes probabilistic methods to assess the influence of climate change on extreme weather events. The World Weather Attribution (WWA) group has successfully employed these techniques, quantifying the impact of climate change on various events within short timeframes. This approach is increasingly adopted by national weather services. However, these probabilistic methods face limitations when dealing with unprecedented events lacking sufficient analogues in historical data. Additionally, variability in results across different climate models and challenges in conveying probabilistic concepts to the public are significant concerns. Storyline methods, offering a more intuitive "what-if" scenario approach, are increasingly used to complement probabilistic assessments. These studies frequently simulate past events under varying climate conditions to understand climate change's role and project future changes.

The study uses a novel automated system that generates near-real-time storyline simulations. The system employs the Alfred Wegener Institute’s Climate Model 2:7 (AWI-CM-1-1-MR), a model known for its realistic representation of climate processes. Storyline simulations are created by constraining (nudging) the observed large-scale atmospheric circulation in the model under different climate scenarios: pre-industrial, present-day, and a future +4°C warmer climate. The system produces five ensemble members for each scenario. The nudging technique focuses on large-scale atmospheric dynamics, allowing the model to freely evolve the smaller-scale processes influenced by thermodynamic changes related to warming. The results, including daily temperature, sea surface temperature, and precipitation data, are disseminated via a publicly accessible online tool (https://climate-storylines.awi.de). The analysis focuses on the rainfall associated with storm Boris, comparing simulations under different climate scenarios to determine the contribution of climate change to the event's intensity and spatial extent. The key metrics assessed include total accumulated precipitation, the area exceeding a 100mm rainfall threshold, and changes in water vapor, vertical velocity, and convective available potential energy. Anomalies are calculated relative to reference periods (1951–2014 for SST and ERA5 climatology for other variables). Changes are expressed as both absolute differences and percentages relative to the present-day simulations, with precipitation changes scaled by 2-meter air temperature changes for comparison with Clausius-Clapeyron scaling.

The storyline simulations successfully reproduced storm Boris's spatial extent and intensity in present-day conditions. The analysis reveals that climate change contributed to about 9% more rainfall during storm Boris compared to a pre-industrial climate. The area experiencing extreme rainfall (>100 mm) was 18% larger than it would have been without human-induced warming. Projections for a future +4°C warmer climate suggest a slight northeastern shift in the storm's core, with a 14% larger area experiencing >100 mm rainfall. Although the increase in total precipitation in the future scenario compared to the preindustrial is not statistically significant, increased water vapor in warmer scenarios suggests exacerbated warming in moisture source regions for the storm. Increases in vertical velocity also likely contributed to the intensification of extreme precipitation. These results demonstrate a clear link between climate change and the increased severity and spatial extent of storm Boris's extreme rainfall. The study's near-real-time system provides timely and accessible climate information during extreme weather events, enhancing climate change communication and informing adaptation strategies.

The findings of this study confirm the substantial impact of climate change on the extreme rainfall event associated with storm Boris. The near-real-time storyline approach successfully quantifies the contribution of anthropogenic warming, offering a clear and easily interpretable message compared to solely probabilistic methods. The increased rainfall and expanded area of extreme precipitation directly highlight the increased risk of devastating floods under a warmer climate. The system's public availability empowers stakeholders and the public with accessible and timely information, promoting informed decision-making for climate change adaptation. While the study relies on a single climate model, future work incorporating a multi-model ensemble will strengthen the robustness of findings. Limitations related to model resolution also need to be considered, as higher-resolution models are needed to capture finer-scale precipitation features, however, using a coupled ocean-atmosphere model provides insight into the interplay between thermodynamic and dynamic processes in determining the event's severity.

This study demonstrates the effectiveness of near-real-time storyline simulations in attributing and projecting climate change impacts on extreme weather events. The application to storm Boris reveals a significant contribution of human-induced warming to the event's severity and spatial extent. The publicly available online tool facilitates effective communication of climate change impacts to both scientific and non-scientific audiences. Future work should focus on incorporating multi-model ensembles and higher-resolution models to refine the accuracy and reliability of the system. Integrating impact modeling into the system will further improve its practical applications for informing adaptation strategies.

The study uses a single climate model (AWI-CM-1-1-MR), which may limit the generalizability of the findings. The model's resolution (~100 km) might not fully capture the finer-scale processes influencing precipitation patterns, and using a single model is always subject to model-specific biases. Future work integrating multi-model ensembles and higher-resolution models is needed to address these limitations and improve the reliability of the results. While the system provides near real-time data, this approach still relies on the model's ability to accurately simulate the large-scale atmospheric circulation, and any inaccuracies in this could affect results.