Ocean warming intensifies marine heatwaves (MHW) globally, increasing their frequency, intensity, duration, and extent, threatening marine ecosystems and economies. However, the latitudinal redistribution of MHW remains unquantified. Analyzing multisource data (1982-2023), we reveal a striking equatorward shift of MHW centroids (MHWC) at an average rate of similar to 1 degrees latitude per decade across the northern and southern Atlantic basins, implying more frequent MHW in lower latitudes. This equatorial shift lacks seasonal phase-locking and is linearly independent of interannual climate variabilities known to influence the Atlantic variations. Mechanism analysis reveals that the Atlantic MHWC shift originates from the interplay between amplified tropical atmosphere-ocean positive feedbacks, which involve sea surface temperature, sea level pressure, and cloud-radiative interactions, and the concurrent weakening of equatorial and coastal upwelling. The attribution analysis shows anthropogenic warming is the dominant driver of this equatorward trend. The observed shift and rising MHW exposure in lower latitudes underscore the necessity for enhanced modeling fidelity at the regional scale, as this shift would worsen biodiversity loss and extreme events.

This study presents a novel unified extreme value theory (UEVT) for simultaneous analysis of positive and negative anomalous events derived from the same anomaly series. This framework characterizes the return level–return period relationship, and by defining the critical and tail intensity of N-year events, quantifies the temporal evolution of their intensity and frequency. Based on the UEVT, an interval extreme value distribution (IEVD) is developed to model both the upper and lower tails of anomaly series and for predicting changes of anomalous events with longer return periods. The UEVT and IEVD demonstrate broader applicability and higher accuracy, and improve practical utility compared to the traditional extreme theory and distributions. The results for N-year temperature anomalies suggest that there is a consistent increase in the intensity and frequency of warm events and a decrease in those of cold events under global warming. Regions exhibiting warming holes or cooling blobs, driven by internal climate variability, offer critical areas for future research on climate extremes. Notably, a southward expansion of warm events from the northern high latitudes and the increasing intensity of warm events in tropical regions show new characteristics of climate change. The hindcast intensity of anomalous events under longer return periods agrees well with the observed trend, and is further used to predict 100-year temperature extremes. Additionally, a new prediction method integrating sliding trend with variability provides a new perspective for modeling non-stationary extremes under strong climatic trends. These methods can be extended to the detection and attribution and applied to the future climate projection with climate models.

Abstract Using a tropical Pacific pacemaker experiment, we demonstrate that the tropical eastern Pacific (TEP) decadal sea surface temperature (SST) variability influences the Atlantic Multidecadal Variability (AMV) through two pathways. The TEP induces a North Atlantic SST tripole response that peaks 1 year later, primarily via modulation of net surface heat flux. The subpolar SST anomalies associated with this tripole response subsequently influence the strength of North Atlantic deep water formation and the Atlantic Meridional Overturning Circulation, ultimately leading to a monopole SST response in the North Atlantic about one decade later. The tripole and monopole responses account for 40% and 59% of the TEP‐forced AMV signal, respectively. The TEP‐forced AMV explains approximately 30% of the variance in the internal AMV, thus substantially modulating the phase transitions and intensity of the historical AMV. This identified TEP forcing should be carefully considered to improve simulations and predictions of the AMV.

Prediction of the southern Indian Ocean dipole (SIOD) used to rely on previous studies on the tropical variabilities, while the extratropical climate systems in the Northern Hemisphere may be unrepresented. This study finds a significant cross-seasonal influence of summer Arctic Oscillation (AO) on the SIOD in the subsequent boreal spring. During high summer AO years, high sea level pressure anomalies in the North Pacific favor the generation of anticyclonic anomalies locally. As a response, the easterly wind anomalies across the tropical Pacific are intensified, which amplifies the zonal dipole of sea surface temperature anomalies stretching the tropical Pacific, modulating the Walker circulation. Then, the surplus precipitation sustains from boreal summer to fall as a result of the anomalous convection coupled with the increased moisture over the western Pacific, which sets an advantageous stage for SIOD initiation through exciting cyclonic (anticyclonic) circulation over the eastern (western) pole of the SIOD in fall. The SIOD begins to grow in boreal winter in the context of the reinforcement of the heat fluxes and oceanic processes, and finally peaks in the following spring. On this basis, an empirical model using the preceding summer AO to predict the subsequent spring SIOD is established. The correlation coefficient between the predicted and observed SIOD is 0.5, which is slightly lower than that in the dynamical models from the North American Multi-model Ensemble Project (0.53), whereas the prediction skills of the former—in terms of the hit rate and false alarm rate—are much higher than the latter in predicting individual SIOD events.

Inter-basin interactions are a pivotal driver of the global climate system. By employing the inter-basin teleconnectivity (IBT) analysis, this study systematically investigates the dominant simultaneous inter-basin linkages across the Pacific, Atlantic, and Indian Oceans in the annual mean sea surface temperature field. We identify 11 distinct inter-basin teleconnections (IBTs), which include two previously recognized patterns, i.e. the boundary current synchronization (BCS) and South Atlantic-South Indian Ocean synchronization (SASI), along with nine new potential IBTs. Two of these new IBTs respectively represent the inter-basin linkages of the Pacific Decadal Oscillation (PDO) and Indian Ocean Basin Mode (IOB) with other ocean basins. We mainly analyze the spatiotemporal characteristics of the remaining IBTs, namely the Bay of Bengal-South Atlantic synchronization (BBSA), Northwest Atlantic-Southeast Indian Ocean seesaw (NASI), Caribbean Sea-Southwest Indian Ocean seesaw (CSSI), Southwest Pacific-Southeast America synchronization (SPSA), North Pacific-South Atlantic seesaw (NPSA), North Tropical Indo-Pacific seesaw (NTIP), and Southern Hemispheric Tripole (SHT). The results demonstrate that these IBTs are statistically relatively independent of some known climate modes and exhibit distinct quasi-periodic characteristics on interannual to decadal timescales. These findings enhance our understanding about inter-basin linkages and interactions.

Seasonal predictability of regional extreme heat depends on how boundary-condition signals shape intrinsic error growth. Here we examine whether May–July (MJJ) Indian Ocean Dipole (IOD) extremes modulate the predictability limit (PL) of summer extreme maximum temperature (TXx) over the North China Plain (NCP). Using ERA5-derived TXx and a nonlinear local Lyapunov exponent framework, we show that both negative and positive IOD extremes slow early TXx error growth relative to neutral IOD conditions. The NCP TXx PL increases from 2.78 months in neutral years to 4.14 months for combined IOD extremes. Composite diagnostics link this extension to organized Indo-Pacific convection, western North Pacific circulation, and local moisture–cloud–radiation constraints. TXx amplitude is not enhanced, indicating that longer predictability arises from constrained local evolution rather than stronger heat anomalies. These results identify MJJ IOD extremes as a boundary-condition context for enhanced seasonal TXx potential predictability.

The El Niño–Southern Oscillation (ENSO) is a dominant mode of interannual climate variability with profound global socioeconomic and ecological impacts1–3. Skillful long-lead prediction of ENSO is critical but remains hindered by nonlinear ocean–atmosphere coupling and the formidable spring predictability barrier (SPB)4–6. While recent deep learning models have shown promise in extending ENSO forecasts beyond a lead time of one year, they largely operate as opaque “black boxes” that mask underlying physical mechanisms. Here we leverage dynamical system deep learning (DSDL)7,8 to construct a fully transparent, multivariate “glass-box” ENSO prediction model. Our model achieves skillful predictions up to 19 months ahead, robustly circumventing the SPB and significantly outperforming state-of-the-art models. Notably, it successfully hindcasts the onset, intensity, and decay of the 2015–2016 super El Niño event more than a year in advance, even when initialized during the boreal spring. Physical interpretation reveals that the “glass-box” DSDL model functions as a multi-basin synergistic prediction framework, which further predicts a potential super El Niño development in 2026–2027. By rendering all model terms explicit, this approach transforms the “black box” into a physically accountable dynamical system, representing a fundamental advance in interpretable climate forecasting.

The total amount of summer extreme precipitation (EP) over the lower reaches of the Yangtze River Basin (LYRB) exhibited a notable transition from a high-value phase in the 1990s (e.g., 1995, 1998, and 1999) to a low-value phase in the 2000s (e.g., 2004, 2006, and 2007). This shift was largely driven by EP days concentrated in June. To investigate the mechanisms behind the shift, daily precipitation from June 1 to 30 during these identified high and low EP years was extracted and subsequently categorized as high precipitation and low precipitation days, respectively. The differences in moisture transport and vertical motion between high and low precipitation days were quantified to identify key drivers from a synoptic-scale perspective. Results show that the dominant moisture sources contributing to the difference come from southern tropical oceans, contributing 57.1–98.9 mm on high precipitation days, compared to only 24.7–53.5 mm on low days. The mechanisms governing vertical motion difference exhibit asymmetry. On high precipitation days, anomalous ascent was dynamically attributed to the upward gradient of vorticity advection and warm advection anomalies. The upward gradient of vorticity advection anomaly resulted from a meridional dipole over the Tibetan Plateau (TP), induced by quasi-zonal wave trains from Europe, along with warm advection anomaly from a TP warm core. Conversely, anomalous descent on low precipitation days was dynamically driven by the downward gradient of vorticity advection and cold advection anomalies. The downward gradient of vorticity advection anomaly occurred due to a negative geopotential height anomaly over the southeastern coast of China, coupled with cold advection from cold anomaly over the eastern TP slope. This research advances our understanding of the EP variability and provides important insights for predicting EP in the densely populated LYRB region.

We demonstrate that the negative phase of the arctic oscillation (AO⁻) and El Niño exert a significant synergistic effect on the enhancement of winter precipitation over southern China (SCWP). The enhancement of SCWP under the co-occurrence of AO⁻ and El Niño arises from both the linear superposition of individual effects and nonlinear interaction between the AO⁻ and El Niño. Physical mechanisms reveal that El Niño favors the negative phase of North Atlantic Oscillation-like circulation, enhancing North Atlantic Rossby wave sources during the concurrent AO⁻. Under the combined influence of AO⁻ and El Niño, Rossby waves initiated from central equatorial Pacific and subtropical North Atlantic synergistically intensified from North Atlantic to Eurasia in three pathways, facilitating the strengthening of a low-pressure anomaly over southern China and amplifies the tropical northwestern Pacific (TNWP) high. The intensified TNWP high is accompanied by stronger descending motion and anticyclonic circulation over the region, which increases southwesterly moisture transport toward southern China, and enhances ascending motion over southern China, leads to increased SCWP. By examining the synergistic effects of AO⁻ and El Niño on SCWP, this study advances our understanding of SCWP variability and its underlying mechanisms.

We traced the origin of very-long-periodic pulsations (VLPs) in type-I burst chains on 2024 February 14. Seven successive and repetitive pulsation structures appeared in radio dynamic spectra in the metric waveband, which were simultaneously measured by CBSm, DART, and MUSER-L. A quasi-period at about 160 -6+11 s, determined by the fast Fourier transform, was detected in the frequency range of about 210-280 MHz. Imaging observations from DART and Solar Dynamics Observatory (SDO) reveal that the type-I burst chains occur above two groups of sunspot umbrae connected by coronal loops. A quasi-period of approximately 170 s was also identified in the sunspot umbrae and coronal loops. The burst chains exhibit strong circular polarization and high brightness temperature, and they show spatiotemporal correlation with emerging magnetic flux. The number densities at the loop top and double footpoints can produce radio emission and generate type-I burst chains in the frequency range of 210-280 MHz. Our observations support the scenario that plasma emission serves as the primary generation mechanism of type-I bursts, with VLPs most likely being modulated by the slow magnetoacoustic waves originating from sunspot umbrae. The observed frequency drift of burst chains may reflect the density attenuation along coronal loops.

The Atlantic meridional overturning circulation (AMOC), a critical component of the global thermohaline circulation, governs meridional heat transport, and connects the sea surface and deep Atlantic. Yet, its dynamical role in the Atlantic multidecadal variability (AMV) and Deep atlantic multidecadal variability (DAMV) remains elusive. We identify a statistically robust coupling between AMV and DAMV, and such coupling supports the interpretation of DAMV as a deep-ocean symbiotic fingerprint of AMV. We further reveal an asymmetric temporal loop: DAMV leads AMV by approximately 1 decade but lags it by 2 to 3 decades, which could be served as deep-ocean connection between AMV phase transition in spatial pattern. This delayed interaction mirrors AMOC-driven ocean heat transport (OHT), particularly the temperature-induced OHT. Our findings demonstrate that AMV and DAMV form a delayed coupled vertical system for the first time, underscoring the importance of deep-ocean dynamics for improving coupled climate prediction on decadal timescales.

Understanding the surge in Central Pacific (CP) El Niño events after the 1990s is a pivotal concern in climate science. However, the relationship between the Atlantic air-sea signals and the CP El Niño remains poorly explained systematically. Here, we show that the Atlantic air-sea signals exhibit a strong correlation with CP El Niño variability after the 1990s. During the initial preconditioning phase, the positive phase of the North Atlantic Oscillation (NAO) in the preceding winter drives the emergence of the spring negative North Tropical Atlantic (NTA) SST anomalies, which trigger the initial warming in the central Pacific. During the development phase, the persistent easterly anomalies in the equatorial eastern Pacific associated with the NTA SST anomalies disrupt the functioning of zonal advection feedback in the equatorial eastern Pacific. As a consequence, this contributes to the formation of a CP El Niño event. Therefore, the increased prevalence of CP El Niño events after the 1990s can be attributed to the unique role of easterly anomalies over the equatorial eastern Pacific, a key process influenced by Atlantic air-sea signals. This finding advances our understanding of CP El Niño dynamics and contributes to more precise climate predictions and meteorological risk assessments.

Abstract As global temperatures rise, widespread warming and fewer cold extremes are generally observed. However, using the metric “temperature drop days”, we identify hemispheric divergent trends in winter cold weather over the past four decades. North America experiences increasing temperature drop days, whereas Europe sees a decline, forming a “Cold North America–Warm Europe” pattern. These opposing trends are linked to atmospheric circulation changes: a weakened but asymmetric tropospheric polar vortex has favored more stable circulation over Europe, while a southward-tilting, meandering polar jet enhances daily circulation variability over North America, increasing temperature drop days. Further analysis indicates that this asymmetry primarily arises from tropical Pacific cooling, with contributions from Arctic sea-ice loss, anthropogenic aerosols, and volcanic forcing. In contrast, greenhouse gas forcing mainly drives the overall weakening of the tropospheric polar vortex. Together, this interplay highlights how internal variability and external forcings shape regional winter cold weather.

Predicting the evolution of nonlinear chaotic dynamical systems is vital across numerous scientific disciplines. However, real-world applications are often hindered by incomplete observational data, characterized by either missing state variables or temporal gaps, posing challenges for traditional data-driven methods that rely heavily on complete inputs or data imputation. The recently proposed Dynamical System Deep Learning (DSDL) method, grounded in Takens’ delay embedding theorem, possesses the capability to infer global system dynamics from partial observations. In this study, we define various scenarios of incomplete information, evaluate the predictive performance of DSDL in two typical scenarios, and compare it with mainstream deep learning methods such as ANN, RC-ESN, LSTM, NG-RC, and SINDy. The results demonstrate that the DSDL method achieves the optimal predictive performance across all scenarios and exhibits the highest stability over multiple trials, indicating its capability to infer global system dynamics from partial information. This method provides a novel perspective for addressing prediction challenges under incomplete information conditions, thereby enriching the research on predicting complex nonlinear dynamical systems. Furthermore, it offers technical support for predicting real-world infinite-dimensional nonlinear chaotic dynamical systems, such as the atmosphere and oceans.

Abstract Extreme cold events (ECEs) impose societal risks, yet the local process linking large-scale teleconnections to regional ECEs is unclear. Here we systematically evaluate local atmospheric factors to identify the factor most strongly associated with the linkage between the boreal winter circum-hemisphere teleconnection and regional ECEs. Using a five-part statistical framework, we compare 1000–500 hPa thickness (ΔZ) with 13 other factors. ΔZ exhibits the strongest and most robust statistical association with ECEs among the factors examined: removing ΔZ produces the largest change in the ECE-related cold-tail distribution, P(ECEs | extreme ΔZ) is the highest among all factors, and the ΔZ–ECE relationship remains robust across multiple statistical analyses. The perturbation hypsometric law and complementary dynamical diagnostics provide a physical interpretation of this statistical relationship. Together, these results suggest that atmospheric thickness provides a physically interpretable local bridge from large-scale teleconnection signals to ECEs.

The Hadley circulation edge (HCE) is an important component of the HC, modulating the subtropical high-pressure distribution and global precipitation pattern. This study investigates the synergistic influence of winter El Niño and Arctic Oscillation negative phase (AO−) on the locations of the northern HCE (NHCE). Results show that both El Niño and AO− are individually associated with equatorward NHCE shifts, but their concurrent occurrence results in a significantly amplified displacement, reaching 1.34° latitude. Mechanistic analysis indicates that El Niño and AO− jointly modulate the variations in eddy momentum flux (EMF), meridional temperature gradient (MTG), subtropical tropopause height (STH), and baroclinic instability criterion (BIC). Under the co-occurrence of El Niño and AO−, anomalous EMF divergence is observed over 15°–30°N, thereby causing an equatorward shift of the convergence center. The El Niño related warming over 5°–30°N and AO− related cooling over 30°-55°N weaken the MTG, combined with a significant decrease in STH over 30°–35°N and enhanced BIC in the latitudinal belt of 25°–35°N. These anomalies together contribute to the equatorward displacement of the NHCE. The synergistic impacts of El Niño and AO− are further established by using 1950–80 datasets. The ridge regression analysis shows that variations in MTG are the dominant contributor to the anomalous NHCE shift. This study underscores the importance of the synergistic effects of climate variabilities in shaping the variations of NHCE latitude, which is important for the crucial role of NHCE latitude in impacting global climate.

Recent advances in artificial intelligence (AI) models have enabled effective simulation of extreme precipitation (EP), yet most models remain black-boxes with unclear contributions from individual meteorological variables. Here, we develop an Explainable Extreme Precipitation Simulation framework (XEP-Sim) to identify and quantify the key drivers of EP in the Asian monsoon region. Six meteorological variables are found to be essential for accurately reproducing EP, with integrated water vapor (IWV) playing a dominant role. EP intensity increases significantly under extreme conditions of these variables, reaching up to 2.8 times higher when all six are extreme compared to none. In addition, interactions between extreme IWV and other variables further amplify EP intensity through dynamic pathways. These findings suggest the dominant role of IWV and its interactions with other key variables, offering physically interpretable insights that can improve EP prediction and inform variable selection in data-driven climate models.

Siberian-Arctic heatwaves(SAHs)disrupt ecosystems by increasing wildfires,thawing permafrost,and threatening Arctic communities.As SAHs become more frequent and intense,accurate prediction is crucial for preparedness and mitigating their impacts.We demonstrate that April surface temperatures in the Siberian Arctic can be predicted one month in advance with a skill of 0.75(1979-2022)using a regression model based on Arctic stratospheric ozone,the Arctic Oscillation,and sea ice in the Kara Sea.This model successfully predicts six of seven SAHs,identifying three driven by extreme ozone depletion and three by significant sea-ice loss.Additionally,from 1979 to 1997,warming was primarily caused by ozone depletion,while from 1998 to 2022,sea-ice loss became the main factor.Our findings indicate that SAHs are predictable and recommend this model for real-time monitoring and forecasting,highlighting its potential to enhance preparedness and reduce adverse effects.

Changes in the translation speed of landfalling tropical cyclones (TCs) pose great challenges in disaster preparedness. While some recent studies have discussed the increased chance of a reduction in the annual-mean translation speed of TCs after landfall, such changes before landfall have not been systematically investigated, especially for short-term variations (that is, hour-to-day timescales). Here we show, first based on observations, that globally, a TC about to make landfall tends to accelerate towards the coast, with an average acceleration of about 0.83 m s−1 per day, which means that the mean translation speed of a landfalling TC increases by 48

Clouds significantly influence Earth’s radiative balance with complex changes in response to surface warming. The key drivers of the changes are the sea surface temperature (SST) pattern effect that reshapes cloud distributions, and the beta feedback that scales low-level fraction change to climatological amounts. Cloud radiative feedback remains the largest source of uncertainty in future climate projections, but current constraints are insufficient. Here, we demonstrate that the percentage change in tropical cloud fraction, driven by spatial patterns in SST increase, is linked to cloud height variations. We introduce a proportional warmer-get-higher paradigm and develop a pattern-based analytical framework, identifying three key factors governing cloud feedback: percentage cloud sensitivity to SST, climatological cloud cover, and SST warming patterns relative to the tropical mean. By leveraging recent observations to constrain these factors in two stages, we establish a process-oriented emergent constraint on projected cloud feedback in the 21st century. The first stage substitutes simulated cloud sensitivity and mean cloud cover to correct biases and reduce the spread by half. Then, the second stage attempts to further constrain the SST pattern effect, which explains 79