Large-scale atmospheric variability can be summarized by recurring patterns called weather regimes. Their properties, including predictability, have been studied using the local dimension, a geometrical estimate of degrees of freedom from multifractal theory. Local dimension estimates vary across regimes, decrease when a single regime dominates, and increase during transitions, supporting their dynamical significance. However, these variations stem not only from geometry but also from sampling density. We develop a null-hypothesis test using stochastic twins-Gaussian mixture-based surrogates matching atmospheric sampling density but with constant geometry-applied to ERA5 500 hPa fields. Density effects alone explain over 25% of local dimension variance and reproduce the dimension drop near regime peaks, indicating this behavior is density-driven, not geometric. The remaining variability is plausibly geometry-driven. This approach, applicable to any observed system with known sampling distribution, offers a new framework for interpreting local dimension estimates in atmospheric and oceanic data.

Stratospheric wave reflection events involve the upward propagation of planetary waves, which are subsequently reflected downward by the stratospheric polar vortex. This phenomenon establishes a connection between the large-scale circulations in the troposphere and in the stratosphere. Here, we investigate a set of wave reflection events characterized by an enhanced difference between poleward eddy heat flux over the northwestern Pacific and equatorward eddy heat flux over Canada. Previous research has pointed to a link between these events and anomalies in the tropospheric circulation over North America, with an associated abrupt continental-scale surface temperature decrease over the same region. In this study, we elucidate the dynamical mechanisms governing this chain of events. We find that the evolution of meridional heat flux anomalies over the northwestern Pacific and Canada around reflection events is explained by a westward-propagating geopotential height ridge and by the downstream development of a trough. The trough advects colder-than-average air southward in the lower troposphere over North America, leading to an abrupt temperature decrease close to the surface. The evolution of this large-scale pattern resembles the shift from a Pacific Trough to an Alaskan Ridge weather regime, with approximately one-third to one-half of such transitions associated with reflection events. Furthermore, stratospheric wave reflection events exert a far-reaching influence on the tropospheric circulation across the northern middle and high latitudes. For example, a few days after the reflection-driven temperature decrease across North America, the North Atlantic jet stream becomes unusually intense and zonal, favoring the occurrence of extreme winds over Europe.

The explosive development of extratropical cyclones and atmospheric rivers plays a crucial role in driving extreme weather in the mid-latitudes, such as compound windstorm-flood events. Although both explosive cyclones and atmospheric rivers are well understood and their relationship has been studied previously, there is still a gap in our understanding of how a warmer climate may affect their concurrence. Here, we focus on evaluating the current climatology and assessing changes in the future concurrence between atmospheric rivers and explosive cyclones in the North Atlantic. To accomplish this, we independently detect and track atmospheric rivers and extratropical cyclones and study their concurrence in both ERA5 reanalysis and CMIP6 historical and future climate simulations. In agreement with the literature, atmospheric rivers are more often detected in the vicinity of explosive cyclones than non-explosive cyclones in all datasets, and the atmospheric river intensity increases in all the future scenarios analysed. Furthermore, we find that explosive cyclones associated with atmospheric rivers tend to be longer lasting and deeper than those without. Notably, we identify a significant and systematic future increase in the cyclones and atmospheric river concurrences. Finally, under the high-emission scenario, the explosive cyclone and atmospheric river concurrences show an increase and model agreement over western Europe. As such, our work provides a novel statistical relation between explosive cyclones and atmospheric rivers in CMIP6 climate projections and a characterization of their joint changes in intensity and location.

Climate-related extreme events impose a heavy toll on humankind, and many will likely become more frequent in the future. The compound (joint) occurrence of different climate-related hazards and impacts can further exacerbate the detrimental consequences for society. By analyzing postprocessed data from the Inter-Sectoral Impact Model Intercomparison Project, we provide a global mapping of future changes in the compound occurrence of six categories of hazards or impacts related to climate extremes. These are: river floods, droughts, heatwaves, wildfires, tropical cyclone-induced winds and crop failures. In line with the existing literature, we find sharp increases in the occurrence of many individual hazards and impacts, notably heatwaves and wildfires. Under a medium-high emission scenario, many regions worldwide transition from chiefly experiencing a given category of hazard or impact in isolation to routinely experiencing compound hazard or impact occurrences. A similarly striking change is projected for the future recurrence of compound hazards or impacts, with many locations experiencing specific compound occurrences at least once a year for several years, or even decades, in a row. In the absence of effective global climate mitigation actions, we may thus witness a qualitative regime shift from a world dominated by individual climate-related hazards and impacts to one where compound occurrences become the norm.

Identifying drought indices that effectively predict future drought impacts remains a critical challenge in seasonal forecasting, as these indices provide the necessary actionable information that enables stakeholders to better anticipate and respond to drought-related challenges. This study evaluates how drought indices balance forecast skill and relevance for estimating impacts across Europe. Using European Centre for Medium-Range Weather Forecasts SEAS5 seasonal predictions and ERA5 reanalysis as benchmarks, we assessed the predictability skill of drought indices over various accumulation periods and their relevance in estimating drought impacts across Europe, with the aim of enhancing impact-based forecasting. To evaluate these relationships, we built upon the findings from a study that utilized drought impact data from the European Drought Impact Report Inventory and employed random forest models to evaluate the significance of various drought indices in predicting sector-specific impacts. Our findings reveal higher predictability in Northern and Southern Europe, particularly during winter and summer, with some regions showing extended predictability up to six months, depending on the season. Focusing on case studies in the UK and Germany, our results highlight regions and seasons where accurate impact predictions are possible. In both countries, high impact predictability was found up to six months ahead, with sectors such as Agriculture, Water Supply, and Tourism in the UK, and Agriculture and Water Transportation in Germany, depending on the region and season. This analysis represents a significant step forward in identifying the most suitable drought indices for predicting impacts across Europe. Our approach not only introduces a new method for evaluating the relationship between drought indices and impacts, but also addresses the challenge of selecting indices for estimating impacts. This framework advances the development of operational impact-based drought forecasting systems for Europe.

The horizontal propagation of Rossby waves in the upper troposphere has been a long-standing topic in dynamical meteorology. The concept of “waveguidability’’, i.e., the capability of the background flow to act as a zonal waveguide for Rossby waves, may prove useful to address this problem, but developing a systematic definition and quantification of such a property remains challenging. With an eye to such issues, the current paper suggests a novel and efficient algorithm to solve the linearized barotropic vorticity equation on a sphere in a forced-dissipative configuration. The algorithm allows one to obtain linear wave solutions resulting from arbitrary combinations of the forcing and the background zonal wind. These solutions can be used to systematically study single- and double-jet configurations and are employed here to show that the latitude of the jet stream does not appear to affect waveguidability. The onset of barotropic instability might hinder the applicability of the linear framework, but it is shown that the nonlinear flow evolution can still be retrieved qualitatively from the linearized solution, both for the stationary component of the wave field and for the temporal evolution of transient waves.

We present an application of quantile generalized additive models (QGAMs) to study spatially compounding climate extremes, namely extremes that occur (near-) simultaneously in geographically remote regions. We take as an example wintertime cold spells in North America and co-occurring wet or windy extremes in Western Europe, which we collectively term Pan-Atlantic compound extremes. QGAMS are largely novel in climate science applications and present a number of key advantages over conventional statistical models of weather extremes. Specifically, they remove the need for a direct identification and parametrization of the extremes themselves, since they model all quantiles of the distributions of interest. They thus make use of all information available, and not only of a small number of extreme values. Moreover, they do not require any a priori knowledge of the functional relationship between the predictors and the dependent variable. Here, we use QGAMs to both characterize the co-occurrence statistics and investigate the role of possible dynamical drivers of the Pan-Atlantic compound extremes. We find that cold spells in North America are a useful predictor of subsequent wet or windy extremes in Western Europe, and that QGAMs can predict those extremes more accurately than conventional peak-over-threshold models. In this paper we propose a new data-driven method to study climate extremes occurring simultaneously in multiple, possibly remote, locations. Such extremes can pose a greater threat to human societies than single, isolated extremes, as their effects may exacerbate each other and lead to correlated losses. The method we suggest requires fewer assumptions than conventional extreme value statistical techniques, and can help us to identify previously unknown relationships between the extremes themselves and their possible drivers. We exemplify its use by studying the co-occurrence of periods of unusually cold weather in North America and subsequent uncommonly strong wind and abundant precipitation in Western Europe. We find that the new method has better predictive power for the European extremes than conventional statistical approaches. Furthermore, we confirm the results of previous studies suggesting an association between the wintertime extremes in North America and Western Europe. Quantile general additive models (QGAMs) can model the relationship between compound climate extremes flexibly and robustlyNorth American cold spells show some predictive skill for wet or windy extremes in Western Europe, even when accounting for confoundersGiven relevant atmospheric predictors, QGAMs can predict these extremes more accurately than peak-over-threshold models in most regions

Advances in impact modeling and numerical weather forecasting have allowed accurate drought monitoring and skilful forecasts that can drive decisions at the regional scale. State-of-the-art drought early-warning systems are currently based on statistical drought indicators, which do not account for dynamic regional vulnerabilities, and hence neglect the socio-economic impact for initiating actions. The transition from conventional physical forecasts of droughts toward impact-based forecasting (IbF) is a recent paradigm shift in early warning services, to ultimately bridge the gap between science and action. The demand to generate predictions of “what the weather will do” underpins the rising interest in drought IbF across all weather-sensitive sectors. Despite the large expected socio-economic benefits, migrating to this new paradigm presents myriad challenges. In this article, we provide a comprehensive overview of drought IbF, outlining the progress made in the field. Additionally, we present a road map highlighting current challenges and limitations in the science and practice of drought IbF and possible ways forward. We identify seven scientific and practical challenges/limitations: the contextual challenge (inadequate accounting for the spatio-sectoral dynamics of vulnerability and exposure), the human-water feedbacks challenge (neglecting how human activities influence the propagation of drought), the typology challenge (oversimplifying drought typology to meteorological), the model challenge (reliance on mainstream machine learning models), and the data challenge (mainly textual) with the linked sectoral and geographical limitations. Our vision is to facilitate the progress of drought IbF and its use in making informed and timely decisions on mitigation measures, thus minimizing the drought impacts globally.

In recent years, deep learning models have rapidly emerged as a stand-alone alternative to physics-based numerical models for medium-range weather forecasting. Several independent research groups claim to have developed deep learning weather forecasts that outperform those from state-of-the-art physics-based models, and operational implementation of data-driven forecasts appears to be drawing near. However, questions remain about the capabilities of deep learning models with respect to providing robust forecasts of extreme weather. This paper provides an overview of recent developments in the field of deep learning weather forecasts and scrutinises the challenges that extreme weather events pose to leading deep learning models. Lastly, it argues for the need to tailor data-driven models to forecast extreme events and proposes a foundational workflow to develop such models.

Climate change is a global challenge with multiple far-reaching consequences, including the intensification and increased frequency of many extreme-weather events. In response to this pressing issue, we present ClimaMeter, a platform designed to assess and contextualize extreme-weather events relative to climate change. The platform offers near-real-time insights into the dynamics of extreme events, serving as a resource for researchers and policymakers while also being a science dissemination tool for the general public. ClimaMeter currently analyses heatwaves, cold spells, heavy precipitation, and windstorms. This paper elucidates the methodology, data sources, and analytical techniques on which ClimaMeter relies, providing a comprehensive overview of its scientific foundation. We further present two case studies: the late 2023 French heatwave and the July 2023 Storm Poly. We use two distinct datasets for each case study, namely Multi-Source Weather (MSWX) data, which serve as the reference for our rapid-attribution protocol, and the ERA5 dataset, widely regarded as the leading global climate reanalysis. These examples highlight both the strengths and limitations of ClimaMeter in expounding the link between climate change and the dynamics of extreme-weather events.