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Northwestern North America has experienced an exceptional heatwave in late June 2021 with many new temperature records across western Canada, Oregon and Washington states. Here we use a recent atmospheric reanalysis and a conditional approach based on dynamical adjustment to assess and quantify the influence of atmospheric circulation and other driving factors to the heatwave magnitude during the June 28–30 period. A blocking anticyclone, enhanced low‐level moisture and clear‐sky downward long‐wave radiation are shown to be the main factors of the heatwave persistence and magnitude. The heatwave magnitude is mainly attributable to internal variability with climate change being an additional factor (10%). Consequences of a similar atmospheric circulation anomaly in different phases of the Pacific Decadal Oscillations and in a warmer world at different global warming levels (1, 2, 3, and 4°C) are explored based on a single model initial‐condition large ensemble. Plain Language Summary Gathering robust statistics and performing extreme event attribution for very rare heat extreme events, such as the 2021 Northwestern North American heatwave, remain challenging due to incomplete sampling of weather data (∼100 years) challenging the application of extreme value theory and caveats related to the use of imperfect climate models in estimating likelihood changes between worlds with and without human influence. Here we use the dynamical adjustment method to quantify the key factors responsible for the magnitude and persistence of the heatwave. Dynamical adjustment aims to identify the causal factors that led to the heatwave with an approach conditional on the observed atmospheric circulation during the event. We find that natural variability is the main driver of the heatwave extreme magnitude with a small contribution from climate change. We also find that the heatwave spatial pattern mainly comes from the atmospheric circulation‐related component (the dynamic component). We investigate a possible contribution due the strong negative phase of the Pacific Decadal Oscillation observed in June 2021 and find it to be rather small. Finally, we ask whether future climate change can make a future similar event even more extreme. We find that the dynamic component would increase by 4°C in a 2°C warmer global climate. Key Points A blocking ridge, enhanced moisture and clear‐sky downward long‐wave radiation are the main causes of the heatwave magnitude and duration The circulation‐induced heatwave component is the main driver of the heatwave pattern and magnitude with a minor role for climate change A similar blocking event in a 2°C warmer climate would lead to a 4°C increase of the circulation‐induced heatwave component

Extratropical cyclones occasionally escalate into devastating windstorms in Western Europe, causing damages worth billions of euros. However, their response to anthropogenic climate change remains uncertain, primarily due to limitations of coarse‐resolution models. This study adopts a high‐resolution, event‐based approach to examine how climate change may enhance Cyclone Anatol (December 1999), using the 2 km HARMONIE‐AROME model within a Pseudo Global Warming (PGW) framework. Results reveal that elevated temperatures amplify wind extremes, both in magnitude and spatial extent, over Denmark and the North Sea, which are linked to increased latent heat release which drives mesoscale instabilities. The findings highlight the potential for even more destructive windstorms in future climates, emphasizing the importance of high‐resolution modeling for understanding these dynamics. While this study does not address changes in cyclone frequency, it underscores the heightened risk of extreme windstorms in a warming world and their implications for disaster preparedness and mitigation strategies.

During July–August 2022, Pakistan (PKT) experienced catastrophic flooding while the Yangtze River Basin (YRB) endured unprecedented heatwaves. While previous studies have examined the physical teleconnections, there remains a critical gap in quantifying the role of anthropogenic forcing in shaping such trans‐regional concurrent extremes. Here, we bridge this gap by combining probabilistic and storyline attribution frameworks to assess both historical and future risks of 2022‐like events. We find that the 2022 event represents a warming‐amplified analogue of the 2010 event, driven by a westward extension of the Western Pacific Subtropical High (WPSH) and an eastward shift of the South Asian High (SAH). Moisture and heat budget diagnosis reveal that dynamically horizontal moisture transport dominated the 2022 PKT precipitation, while surface cloud‐radiative forcing drove the YRB heatwave. Using complex network analysis, we uncover intensified cross‐regional linkages under SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5 scenarios. Crucially, our bivariate probabilistic attribution indicates that anthropogenic forcing accounts for nearly 100% of the likelihood of the 2022 event. Projections show that, by 2071–2100, the probability of such events could rise by 57–326 times, relative to a baseline probability of 0.0015 in historical simulations. Further, storyline attribution demonstrates that anthropogenic thermodynamics and circulation dynamics contributed approximately 60% and 40% to the 2022 event, with nearly half of the dynamic effect attributable to anthropogenic forcing. These results offer a quantitative perspective on the rising risk of concurrent Pluvial Pakistan–Hot Yangtze events under climate change, offering valuable insights for regional climate resilience and adaptation planning.

We present a framework for developing storylines of UK sea level rise to aid risk communication and coastal adaptation planning. Our approach builds on the UK national climate projections (UKCP18) and maintains the same physically consistent methods that preserve component correlations and traceability between global mean sea level (GMSL) and local relative sea level (RSL). Five example storylines are presented that represent singular trajectories of future sea level rise drawn from the underlying large Monte Carlo simulations. The first three storylines span the total range of the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) likely range GMSL projections across the SSP1-2.6 and SSP5-8.5 scenarios. The final two storylines are based upon recent high-end storylines of GMSL presented in AR6 and the recent literature. Our results suggest that even the most optimistic sea level rise outcomes for the UK will require adaptation of up to 1 m of sea level rise for large sections of coastline by 2300. For the storyline most consistent with current international greenhouse gas emissions pledges and a moderate sea level rise response, UK capital cities will experience between about 1 and 2 m of sea level rise by 2300, with continued rise beyond 2300. The storyline based on the upper end of the AR6 likely range sea level projections yields much larger values for UK capital cities that range between about 3 and 4 m at 2300. The two high-end scenarios, which are based on a recent study that showed accelerated sea level rise associated with ice sheet instability feedbacks, lead to sea level rise for UK capital cities at 2300 that range between about 8 m and 17 m. These magnitudes of rise would pose enormous challenges for UK coastal communities and are likely to be beyond the limits of adaptation at some locations.

Two methods exist to address the degree to which past extreme events and associated disasters will be intensified due to climate change: storyline approaches and risk-based approaches. However, the risk-based approach applied to weather similar to the target event (typhoons, a stationary weather front,…etc) becomes theoretically similar to the storyline approach. We examine this theory for the climate change impact of a real event, Typhoon Hagibis, which caused devastating flood damage to eastern Japan in 2019, while focusing on basin-averaged accumulated rainfall (BAAR) in major eastern river basins. A risk-based approach was conducted to determine the future change of BAAR by calculating the quantile change corresponding to Hagibis from the probability distribution of typhoon-induced events in a large ensemble climate simulation dataset database for Policy Decision-making for Future climate change (past, +2K and +4K future climates). A storyline approach for Typhoon Hagibis was realized using a pseudo global warming (PGW) experiment with a 5 km non-hydrostatic model. The projected BAAR in the two approaches were consistent for all target basins, supporting the robustness of the calculated changes in extreme catchment precipitation. This presents an important practical benefit: one can assess future climate change impact on a past symbolic event using either PGW experiments or large ensemble climate projections for the target weather.

In the US, extreme heat causes the largest weather-related deaths, and their frequency is projected to increase under global warming. We apply a storyline approach to identify coherent narratives of the spatial patterns of heatwave days in the contiguous United States (CONUS) in a 3 °C warmer climate. We use daily temperature data from the seamless system for prediction and earth system research large-ensemble climate model under the shared socio-economic pathway 5–8.5. We find that CONUS surpasses the 3 °C-warming threshold (relative to 1921–1950) between the mid-2020s and early 2040s across all 30 ensemble members. Despite similar spatial patterns of annual mean surface temperatures over the CONUS in the 3 °C-crossing years, the simulated heatwave days are clustered into four distinct spatial patterns: Southeastern, Mountain-Central, Western-Severe, and Western-Mild. The Western-Mild pattern is the most frequent (19 out of 30 realizations) with the fewest annual heatwave days, while the remaining patterns tend to experience a greater number of annual heatwave days and are less common (3 or 4 out of 30). We present four case studies corresponding to the representative clusters to better understand local extreme heat storylines, highlighting how the maximum annual mean temperature at the local scale might differ from the continental scale. Specifically, we find very severe potential conditions in the 3 °C-passing years with parts of the US potentially experiencing more than 150 cumulative heatwave days. This study implies that careful consideration of local temperature changes will be necessary to provide various climate adaptation policy perspectives.

The aim of this work is the prediction of heat-related mortality for Germany under future, i.e. hotter, climate conditions. The prediction is made based on 2m temperature data from climate storyline simulations using machine learning techniques. We use an echo state network for linking the outputs of storyline climate simulations to the target data. The target data are all-cause mortality rates of Germany for all ages. The network is trained with present day climate model outputs. Model outputs of future, i.e. 2K and 4K warmer, storylines are used to predict mortality rates under such climatic conditions. We find that we can train an echo state network with recent temperature data and mortality and make plausible predictions about expected developments of mortality in Germany based on future climate storylines. The trained network can successfully predict mortality rates for future climate conditions. We find increased mortality during the summer months which is attributed to the presence of more severe heat waves. The mortality decrease found during winter can be explained milder winters leading to fewer deaths caused by respiratory diseases. However, mortality in winter is largely influenced by other factors such as influenza waves or vaccination rate and explainability due to temperature is limited.

Plausible future streamflow time series are essential for evaluating policies and management strategies in river basins and testing the operation of water resource systems. Relying solely on stationary historical data is not sufficient in a changing climate. However, uncertainty in the range of streamflow projections from General Circulation Models calls into question their direct use in water resources planning. An intermediate approach is needed to identify ensembles of streamflow time series based on well‐defined assumptions that represent plausible future hydrologic conditions. This paper suggests multiple quantitative storylines of plausible future conditions, each matched with a representative streamflow ensemble to serve as inputs for planning models where, to account for uncertainty, plans or policies that are robust to a range of plausible futures are developed. Applying this approach in the Colorado River Basin we found that, while three storylines were well matched with existing ensembles, there was no suitable ensemble representing increasing variability around a declining mean. To address this gap, we developed a general method to create new streamflow ensembles that account for future changes by combining observed and paleo‐reconstructed flows and adjusting the marginal distribution of the streamflow time series to incorporate the estimated decline in, and increasing variability of, future flow. The results are a set of quantitative storylines that justify a range of plausible future conditions, and a new warming‐driven declining streamflow ensemble for use in Colorado River Basin scenario evaluation and decision‐making representing the plausible increasing variability around a declining mean storyline. Plain Language Summary Plausible scenarios for future streamflow are crucial for making decisions about how to manage water resources. Relying on simulations based on past data is not enough because the climate is changing. However, streamflow projections from climate models are limited due to uncertainty, making it hard to know which to use directly in decision making. An intermediate strategy is needed to identify sets of streamflow time series based upon reasonable assumptions for future hydrologic conditions. We developed quantitative storylines that quantify assumptions about future streamflow conditions and then sought ensembles—or sets of time series generated by a single method—that match these storylines. These representative ensembles provide inputs needed to plan across a range of specific plausible future conditions to account for uncertainty. Applying this approach in the Colorado River Basin, we used a broad range of metrics to identify streamflow ensembles that best represent each storyline. For a case where there was not a prior ensemble consistent with a plausible future conditions storyline, we developed a general approach to create a new streamflow ensemble by adjusting a stationary time series to incorporate the estimated decline in and increasing variability of future flow. Key Points We developed quantitative storylines of plausible future streamflow conditions and identified their representative ensembles The storyline of increasing variability around a declining mean is not well matched among currently available ensembles We present a novel method to fill this gap by generating a warming‐driven declining streamflow ensemble with adjusted paleo‐conditioning

Storylines are physically plausible scenarios of future climate change, statistically derived from an ensemble of climate model projections and organized according to the magnitude of projected changes in two or more remote drivers that strongly influence the spatial pattern of the climate response. Here, we provide novel insights into the Arctic storylines identified by Levine et al. (2024), where Barents-Kara Sea warming and lower-tropospheric Arctic warming during the extended summer season (May–October) were remote drivers, as we identify a set of models from the Coupled Model Intercomparison Project phase 6 to represent the storylines. We do this by first identifying models that are similar to these storylines in terms of each remote driver response and quantifying this similarity. Second, we evaluate the model’s performance in terms of a simple performance score based on the mean normalized root-mean-square error for multiple climate variables of importance for the storylines. The normalized values vary between 0 and 1 for all variables, allowing them to exert a comparable influence on the score. The advantage of the score is that it provides an easily implementable and interpretable way of identifying models that are characterized by large errors relative to the rest of the ensemble. Finally, we combine the similarity estimate and the score to select models to represent the storylines. We focus on the Arctic during the extended summer season for which the storylines were designed, but also consider other seasons and regions. Through this exercise, we also document the methodology, benefits, and limitations of the score.

This paper explores how trans people who make transitions negotiate their gendered bodies in different moments of this process, and how their narrative storylines are emplotted in physical activity and (non)organized sports (PAS) participation. A qualitative semi-structured interview-based study was developed to analyze the stories of eight trans people (three trans women, two trans men, and three nonbinary persons) who participated in PAS before and during their gender disclosure. A thematic analysis was conducted to identify the patterns in the transition process and the structural analysis of the stories from the interviews. Three transition moments (the closet, opening up, and reassuring) were identified from the thematic analysis. Most participants showed difficulties in achieving their PAS participation during the two earlier moments. The predominance of failure storylines was found particularly in men, while success was more likely to appear in women because their bodies and choices fitted better with their PAS gender ideals. The nonbinary trans persons present alternative storylines in which corporeality has less influence on their PAS experiences. The knowledge provided on the moments and the stories of transition help to explain trans people’s (non)involvement in PAS and to guide policymaking and professional action in PAS fields.