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This study unravels the characteristics, mechanisms, and predictability of four consecutive record‐breaking heatwaves hitting North China in June and July 2023. The first three heatwaves primarily influenced the northern part of North China and were accompanied by consistent anticyclonic anomalies in the upper troposphere. The anomalous anticyclone was caused by the British–Baikal corridor teleconnection along the polar front jet, particularly during the second heatwave. In contrast, the fourth heatwave was induced by a distinct low‐pressure system, attributed to the Silk Road pattern along the subtropical jet. The presence of this low‐pressure system and its interaction with atmospheric rivers and local topography led to the foehn wind, further contributing to the rise in surface temperatures. Sub‐seasonal to seasonal models can effectively predict the occurrence of all heatwaves 2–5 days in advance despite underestimating the intensity. However, models exhibit limitations in providing reliable predictions when the lead time exceeds 2 weeks. Plain Language Summary In the summer of 2023, North China experienced four consecutive extreme high‐temperature events, which are called heatwaves. This study investigates the main factors that cause the four events and shows how well the operational numerical models can predict the heatwaves. The first three heatwaves shared similar circulation, with high‐pressure systems controlling North China. This local circulation anomaly was related to an upstream quasi‐stationary wave train along the polar front jet. The fourth heatwave was associated with a low‐pressure system over North China and an upstream quasi‐stationary wave train along the subtropical jet. Moreover, predictions from operational models demonstrate their capability to forecast the occurrence of high temperatures 2–5 days ahead, with an underestimation in the intensity. However, models are not reliable when it comes to predicting heatwaves 2 weeks in advance. Key Points North China witnessed record‐breaking heatwaves in June and July 2023, consisting of four sequential synoptic‐scale events The first three and the last heatwaves are controlled by distinct local circulations induced by atmospheric wave trains across Eurasia S2S models capture the heatwave occurrences 2–5 days in advance, but predictive skill notably diminishes beyond 2 weeks

The El Niño–Southern Oscillation (ENSO) has a significant impact on the surface climate of East Asia by modulating the atmospheric circulation over the Kuroshio Extension. Here, we show that the ENSO–East Asia teleconnections are strongest in early winter due to the combined effects of the Indian Ocean and Maritime Continent convections, but weakest in mid‐winter as these tropical convections weaken. During the early El Niño winter, convection is enhanced over the Indian Ocean and suppressed over the Maritime Continent. The associated Rossby wave trains constructively interfere over the Kuroshio Extension, resulting in anticyclonic circulation anomalies. The equatorial central Pacific convection has a minimal impact on the East Asia. This result suggests that the Indian Ocean and the Maritime Continent convections, rather than the equatorial central Pacific convection, are the precursors of the early winter ENSO–East Asia teleconnections, and need to be considered for subseasonal‐to‐seasonal prediction in East Asia. Plain Language Summary The El Niño–Southern Oscillation (ENSO) significantly influences the winter surface climate in East Asia through atmospheric teleconnections. The ENSO teleconnections are particularly pronounced in early winter when an anomalous anticyclonic circulation developes over the Kuroshio Extension and transports warm and moist air to the East Asian continent, but weaken in mid‐winter. Here, we show that such subseasonal variability of the ENSO–East Asia teleconnections is primarily determined by tropical convection over the Indian Ocean and the Maritime Continent, with a minimal impact of convection over the equatorial central Pacific where ENSO develops. Key Points The El Niño‐Southern Oscillation (ENSO) teleconnections show distinct subseasonal variability over East Asia, with the strongest teleconnections in early winter The ENSO–East Asia teleconnections are modulated by anomalous tropical convection over the Indian Ocean and the Maritime Continent on subseasonal time scale Rossby wave trains induced by enhanced convection over the Indian Ocean and those by suppressed convection over the Maritime Continent constructively interfere over the East Asia during the early El Niño winter

Extra‐tropical cyclones are an important source of weather variability in the mid‐latitudes. Multiple occurrences in a short period of time at a particular location are denominated serial cyclone clustering (SCC), and potentially lead to large societal impacts. We investigate the relationship between SCC affecting Western Europe and large‐scale weather regimes (WRs) in the North Atlantic‐European region in boreal winter. We find that SCC in low latitudes (45°N) is predominantly associated with the anticyclonic Greenland Blocking WR. In contrast, SCC in mid and high latitudes (55°N, 65°N) is mostly linked to different cyclonic WRs. Thereby, SCC occurs typically within a well‐established WR that builds up prior to SCC and decays after SCC. Thus, SCC events are closely associated with recurrent, quasi‐stationary and persistent large‐scale flow patterns (WRs). This mutual relationship reveals the potential of WRs in forecasting storm series and associated impacts on sub‐seasonal to seasonal time scales. Plain Language Summary Serial cyclone clustering describes the occurrence of multiple extra‐tropical cyclones within a certain time frame and a spatially restricted region. Since extra‐tropical cyclones can be associated with strong winds and heavy precipitation, multiple occurrences can lead to large cumulative impacts in the affected areas. We analyze the relationship between serial cyclone clustering (SCC) in Western Europe and so‐called weather regimes (WRs) in the North Atlantic‐European region in boreal winter. These regimes describe slow evolving and enduring large‐scale atmospheric circulation patterns. Relationships with certain regime types are identified but depend on the latitude at which the clustered frequency of extra‐tropical cyclones is found. When SCC occurs in low latitudes (45°N), it mostly appears coincident with anticyclonic large‐scale flow patterns. In contrast, SCC in mid and high latitudes (55°N, 65°N) often occurs simultaneously with different cyclonic regimes. We find that periods of SCC occur typically within WR life cycles pointing to the fact that both, the WRs and SCC periods, are interlinked. This relationship may facilitate forecasting storm series and associated impacts on time scales beyond 2 weeks. Key Points A close relationship is found between serial cyclone clustering (SCC) at 5°W and weather regimes (WRs) in the North Atlantic‐European region SCC in mid and high latitudes (55°N, 65°N) is mainly associated with cyclonic and in low latitudes (45°N) with anticyclonic WR life cycles Regardless of the selected latitude, SCC occurs mostly during an active regime life cycle and is manifested in a well‐established WR

The European winters of 2017–18 and 2018–19 were not climatically extreme, but both winters had a major sudden stratospheric warming (SSW). In February 2018, an SSW led to an intense cold outbreak across Europe and further spells of cold weather in March. The SSW of January 2019, although well predicted and expected to increase the chance of a cold end to winter, apparently produced little impact. In this study, we examine the performance of the Met Office seasonal prediction system in these winters, and the influences that led to these outcomes. To achieve this latter objective, sets of numerical experiments are performed in which the tropical troposphere and the extratropical stratosphere are relaxed towards their observed state, allowing the influence of each on the North Atlantic‐European atmospheric circulation to be identified. Using these experiments, we show that the SSWs had similar impacts in each case, creating a signal of easterly surface wind anomalies in the weeks following the event. In contrast, tropical influences were opposite in the two winters, acting to strengthen the easterly signal at the end of February 2018 and opposing it in January 2019. The different apparent responses to the two events therefore came about largely through tropical tropospheric variability. Furthermore, we highlight the importance of a very strong cycle of the Madden‐Julian Oscillation (MJO) in late January and early February 2018 as an important driver for the February 2018 SSW. MJO teleconnections appear to have been critical in creating the large mid‐latitude wave 2 amplitude that has been identified as the immediate cause of this event. In February 2018, a strong negative North Atlantic Oscillation brought freezing weather to Europe following a sudden stratospheric warming (SSW). Yet the January 2019 SSW apparently had only a small impact. Here we explain these differences as the result of prevailing tropical influences—aiding cold weather in one case and inhibiting it in the other. Furthermore, we reveal a key role for the Madden‐Julian Oscillation in triggering the February 2018 SSW that led to European cold spell.

Few studies have utilized machine learning techniques to predict or understand the Madden‐Julian oscillation (MJO), a key source of subseasonal variability and predictability. Here, we present a simple framework for real‐time MJO prediction using shallow artificial neural networks (ANNs). We construct two ANN architectures, one deterministic and one probabilistic, that predict a real‐time MJO index using maps of tropical variables. These ANNs make skillful MJO predictions out to ∼18 days in October‐March and ∼11 days in April‐September, outperforming conventional linear models and efficiently capturing aspects of MJO predictability found in more complex, dynamical models. The flexibility and explainability of simple ANN frameworks are highlighted through varying model input and applying ANN explainability techniques that reveal sources and regions important for ANN prediction skill. The accessibility, performance, and efficiency of this simple machine learning framework is more broadly applicable to predict and understand other Earth system phenomena. Plain Language Summary The Madden‐Julian oscillation (MJO)—a large‐scale, organized pattern of wind and rain in the tropics—is important for making weather and climate predictions weeks to months into the future. Many different numerical models have been used to study the MJO, but few works have examined how machine learning and artificial intelligence methods can predict and understand the oscillation. In this work, we show how two different types of machine learning models, called artificial neural networks, perform at predicting the MJO. We demonstrate that simple artificial neural networks make skillful MJO predictions beyond 1–2 weeks into the future, and perform better than other statistical methods. We also highlight how neural networks can be used to explore sources of prediction skill, via changing what variables the model uses and applying techniques that identify regions important for skillful predictions. Because our neural networks perform relatively well, are simple to implement, are computationally affordable, and can be used to inform scientific understanding, we believe these methods are more broadly applicable to study other important climate phenomena aside from just the MJO. Key Points Simple machine learning models are an efficient, flexible tool to predict and study the Madden‐Julian oscillation (MJO) Shallow neural networks skillfully predict an MJO index out to ∼18 days in winter and ∼11 days in summer, outperforming linear models Varying ANN input and using explainable artificial intelligence methods offer insights into the MJO and key regions for prediction skill

This study explores Arctic warming's effect on Eurasia's temperature variability, notably the warm Arctic–cold Eurasia (WACE) pattern, and assesses seasonal prediction models' accuracy in capturing this phenomenon and its monthly variation. Arctic warming events are categorized into deep Arctic warming (DAW), shallow Arctic warming (SAW), warming aloft (WA), and no Arctic warming (NOAW), based on the temperatures at 2 m and 500 hPa in the Barents–Kara Sea. It is revealed that DAW events are significantly correlated with monthly cold temperature anomalies in East Asia, predominantly occurring in January–February, excluding December. This study evaluates two primary capabilities of seasonal prediction models: their proficiency in forecasting these Arctic warming events, particularly DAW, and their ability to replicate the spatial patterns associated with DAW. Some models demonstrated notable predictive skill for DAW events, with enhanced performance in January and February. Regarding spatial pattern reproduction, models showed limited alignment with the reference dataset over the Northern Hemisphere (above 25° N) in December, whereas a higher degree of concordance was observed in January–February. This indicates their capability in capturing the atmospheric circulation patterns associated with DAW, pointing to areas where model performance can be enhanced. The multi‐model ensemble (MME) was adept at predicting Arctic warming events but could not reproduce circulation patterns. Because the MME is including models other than the six individual models, the teleconnection patterns could be buried. Generally, the models performed better during January and February, as they mostly showed WACE‐related patterns when DAW events were expected (not shown); however, WACE patterns were not significant in the reanalysis data during December.

Sudden stratospheric warmings (SSWs) during boreal winter are one of the main drivers of sub‐seasonal climate variability in the Northern Hemisphere. Although the impact of SSW events on surface climate and climate extremes has been clearly demonstrated, the impact of the resulting climate anomalies on society has not been so widely considered. In the United Kingdom (UK), SSWs are associated with cold weather, which is linked to significant increases in mortality. This study demonstrates, for the first time, that SSWs are linked to increases in mortality in the UK. A distributed lag nonlinear model and standard parameter settings from the literature is used to construct a daily time series of UK deaths attributable to cold weather between 1991 and 2018. Weekly mortality associated with SSWs is diagnosed using a superposed epoch analysis of attributed mortality for the 15 SSW events in this period. SSW associated mortality peaks between 3 and 5 weeks after SSW central date and leads to, on average, 620 additional deaths in the same period. Given that the impacts of SSWs can be skilfully predicted on sub‐seasonal timescales, this suggests that health and social care systems could derive substantial benefit from sub‐seasonal forecasts during SSWs. Sudden stratospheric warming events are dramatic, mid‐winter changes to the flow in the Northern Hemisphere that lead to widespread changes to surface climate. In particular, in Northern Europe, temperatures are colder than average and cold extremes are enhanced. In this study, we quantify the impact of SSW‐associated cold waves with mortality in the United Kingdom. Using mortality data from all regions of the UK, and temperature data from the new HadUK‐Grid dataset, we can produce national and regional estimates of the impacts of cold temperatures on the risk of mortality as shown in the figure below. For days following SSW events, there is an increase in cold temperatures and in the number of days with enhanced mortality risk (shown by comparing the PDFs for days that follow SSWs in the black lines and all other days in the green shading). We show that SSWs, on average, result in 620 additional deaths in the UK, with much of this additional mortality occurring 3–5 weeks after the event. Given that sub‐seasonal predictability is enhanced following SSW events, they could provide a very useful tool for public health authorities to mitigate the worst impacts of prolonged cold waves.

The Madden–Julian Oscillation (MJO) is an influential climate phenomenon that plays a vital role in modulating global weather patterns. In spite of the improvement in MJO predictions made by machine learning algorithms, such as neural networks, most of them cannot provide the uncertainty levels in the MJO forecasts directly. To address this problem, we develop a nonparametric strategy based on Gaussian process (GP) models. We calibrate GPs using empirical correlations and we propose a posteriori covariance correction. Numerical experiments demonstrate that our model has better prediction skills than the artificial neural network models for the first five lead days. Additionally, our posteriori covariance correction extends the probabilistic coverage by more than 3 weeks. Plain Language Summary The Madden–Julian Oscillation, or MJO, is a significant weather pattern that affects weather, influencing rainfall, temperature, and even storm frequency and intensity. When the MJO is active, it can affect the weather globally. To better predict weather changes with 3–4 weeks in advance, we rely on the ability to predict the MJO's activity. Data‐driven methods such as the ones that rely on deep neural networks have been recently employed to make such predictions. By examining existing MJO patterns, neural networks attempt to predict upcoming ones. However, while neural networks are robust enough to predict the MJO's activity, they do not provide confidence intervals for those predictions. To address this shortcoming, we use a model known as the “Gaussian process” or GP. This statistical tool is distinctive because it not only provides predictions but also quantifies the level of confidence in them. Key Points Propose a probabilistic framework for Madden–Julian Oscillation prediction using Gaussian process models and empirical correlations Nonparametric model has a better prediction skill than artificial neural network for the 5 forecast lead days in terms of correlation and overall in terms of root mean squared error The Gaussian process model provides the confidence intervals for the forecast at subseasonal scales (3 weeks) on average

The prediction of the weather at subseasonal‐to‐seasonal (S2S) timescales is dependent on both initial and boundary conditions. An open question is how to best initialize a relatively small‐sized ensemble of numerical model integrations to produce reliable forecasts at these timescales. Reliability in this case means that the statistical properties of the ensemble forecast are consistent with the actual uncertainties about the future state of the geophysical system under investigation. In the present work, a method is introduced to construct initial conditions that produce reliable ensemble forecasts by projecting onto the eigenfunctions of the Koopman or the Perron‐Frobenius operators, which describe the time‐evolution of observables and probability distributions of the system dynamics, respectively. These eigenfunctions can be approximated from data by using the Dynamic Mode Decomposition (DMD) algorithm. The effectiveness of this approach is illustrated in the framework of a low‐order ocean‐atmosphere model exhibiting multiple characteristic timescales, and is compared to other ensemble initialization methods based on the Empirical Orthogonal Functions (EOFs) of the model trajectory and on the backward and covariant Lyapunov vectors (CLVs) of the model dynamics. Projecting initial conditions onto a subset of the Koopman or Perron‐Frobenius eigenfunctions that are characterized by time scales with fast‐decaying oscillations is found to produce highly reliable forecasts at all lead times investigated, ranging from one week to two months. Reliable forecasts are also obtained with the adjoint CLVs, which are the eigenfunctions of the Koopman operator in the tangent space. The advantages of these different methods are discussed. Plain Language Summary Weather forecasts often reach their limit of predictability at one to two weeks. In order to extend forecast skill beyond this two week limit, the weather prediction community has begun transitioning to the use of coupled models that include both atmosphere and ocean dynamics, with the slower ocean dynamics enabling an extended forecast horizon. Due to uncertainties in the accuracy of the initial conditions and the model itself, such forecasts must be probabilistic. The primary approach for probabilistic weather prediction is to generate ensemble forecasts that integrate multiple copies of the model started from slightly different initial conditions. Here we show that the method used to determine the ensemble of initial conditions has a significant impact on the probabilistic forecast skill at horizons ranging from a few weeks to a few months. We show that many of the existing techniques used for short forecasts are suboptimal for longer forecast horizons. We introduce a new perspective and corresponding techniques that permit the initialization of these ensemble forecasts using information that is intrinsic to the nature of the evolution of the coupled system dynamics, and present data‐driven methods that allow this information to be estimated directly from historical data. Key Points Several methods for initializing ensemble forecasts with long lead times are tested in the context of an ocean‐atmosphere coupled model The methods providing the most reliable ensembles are the adjoint Lyapunov vectors and the adjoint modes of the Dynamic Mode Decomposition These vectors are related to the eigenfunctions of the Koopman and Perron‐Frobenius operators of the system

Precipitation predictions in the sub-seasonal timescale are very important for several sectors such as agriculture and energy in regions of South America that are very much affected by precipitation extremes, both excess and lack of rain. The aim of the present study is to investigate the ability of two S2S project models (ECMWF and NCEP) to detect the Southern Hemisphere teleconnections in model hindcasts and the associated anomalous precipitation over South America. The period of analyses is 1999–2010 for the austral summer season (December–January–February). Both models represented adequately the Southern Annular Mode (SAM) pattern in predictions up to 4 weeks ahead and the Pacific South America (PSA) pattern up to 3 weeks. Atmospheric variables of observed extreme cases of SAM were well predicted by the two models, 2 and 3 weeks in advance. The models predicted well atmospheric variables in observed extreme cases of PSA, 2 weeks in advance and with less intensity in the third week. Precipitation anomaly signals associated with these modes were well predicted 2 weeks in advance, although with different intensities. The good ability of the models hindcast in predicting teleconnection patterns and precipitation anomalies over South America provides more confidence to use predictions at sub-seasonal timescale.