Siberian High variability shaped the record-breaking winter extreme temperature variation over eastern China in 2023–2024

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Zwiers, Guangxin He, Jingjia Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9413098/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Rapid reversals between warm and cold extremes, sometimes known as extreme temperature variation, are highly-impact climate variability in mid-latitude regions. During the winter of 2023–2024, eastern China experienced its strongest day-to-day surface air temperature variability since 1979. Using ERA5 reanalysis for 1979–2024, we investigate the dynamical and thermodynamic processes underlying this record-breaking event. The results show that intraseasonal variability of the Siberian High (SBH) played a dominant role in this process. Large intraseasonal variability of the SBH reorganized low-level circulation over eastern China and modulated alternating intrusions of cold air from polar and warm air from tropics. The variation of East Asian Trough (EAT) over the Bohai Sea region followed the SBH, as indicated by lead–lag analysis. This consists a vertically coupled pathway reinforced by southeastward-propagating wave train. Thermodynamic diagnosis reveals a marked north–south contrast in the surface temperature anomalies. In North China, temperature variability was governed mainly by diabatic heating linked to SBH–EAT coupling. By contrast, meridional temperature advection and diabatic heating contributed jointly to warm-cold shifting in South China. A long-term comparison further shows that the temperature influence of EAT variability becomes pronounced only under strong SBH variability, validating the SBH as the leading regulator of this winter extreme temperature variation event over eastern China. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences extreme temperature variation atmospheric circulation temperature budget eastern China Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction According to the Intergovernmental Panel on Climate Change (IPCC), global climate change is significantly increasing the frequency and intensity of extreme weather and climate events 1 . The increasing in frequency in record breaking extreme events 2-8 , has garnered growing attention from both the scientific community and risk management sectors. The intensity or duration of these extremes frequently exceeds historical thresholds 9-12 , leading to impacts on ecosystems and human societies that surpass those caused by normal climatic fluctuations. Existing research has largely concentrated on individual extreme events 13 , frequently overlooking the rapid changes that often precede them . Also, extremes frequently often occur in compound forms, characterized by multiple events happening simultaneously or in rapid succession 14 . A particular form of compound event involves an abrupt reversal of conditions from one extreme to another, which is exemplified by rapid shifts in temperature 15 , precipitation 16, 17 , and atmospheric circulation 18, 19 . Amongst these, the rapid shift from extreme warmth to extreme cold, or vice versa, is specifically termed "temperature whiplash" and is defined as an extreme temperature variation event. High-impact temperature whiplash events have been recorded multiple times worldwide in recent years. Two, of many, notable examples include the abrupt cooling in North America during spring 2012 following an unusually warm period, which damaged crops 20 , and a 24-hour shift from a heatwave to a blizzard in the Rocky Mountains in 2020 21 . Such cases underscore the need to better understand the physical processes governing rapid transitions between temperature extremes. Previous studies have suggested that global climate change exerts a significant influence on regional weather whiplash events, primarily through the modulation of large-scale atmospheric circulations 22-28 . Consequently, the identification of intrinsic atmospheric circulation modes associated with weather whiplash is important for assessing the potential impacts of global climate change on these phenomena. Extreme temperature variation is defined by pronounced shifts between anomalously low and high temperature conditions. Research has shown that these events occur most frequently in mid-latitude regions, such as East Asia and eastern North America, where they are often more intense and exhibit shorter transition times than elsewhere. This phenomenon is primarily driven by enhanced atmospheric Rossby wave activity and jet stream meandering 29, 30 . For instance, the southeastward propagation of Rossby wave energy from source regions like the Kara Sea or Northern Europe can trigger wave trains characterized by alternating high- and low-pressure anomalies that induce sharp temperature reversals 31, 32 . Persistent blocking highs, such as Ural and Okhotsk blocking, play a central role in guiding cold air southward, thereby facilitating transitions between temperature states 33 . From a broader perspective, extreme temperature whiplash can be conceptualized as a rapid transition between persistent large-scale circulation patterns, such as abrupt alternation between distinct 500 hPa geopotential height anomalies 18 . Furthermore, changes in the position and intensity of the mid-latitude jet stream, coupled with shifts in storm tracks, have also been identified as key determinants of the frequency and intensity of extreme variations 34 . Underlying the large-scale circulation background, local physical processes serve to amplify or buffer near-surface temperature changes. Temperature advection constitutes a core process; in winter, warm-to-cold whiplash events are primarily driven by strong cold advection associated with anomalous northerly winds 35 . Cloud cover and associated radiative feedbacks also play a critical role. During a transition, increased cloud cover can promote cooling by attenuating incoming solar shortwave radiation; conversely, decreased cloud cover can facilitate warming by enhancing solar insolation 32 . It should be noted that clouds also trap outgoing longwave radiation, which may buffer cooling, a complex feedback mechanism that requires careful diagnostic analysis. Furthermore, land-atmosphere interactions, such as soil moisture-atmosphere feedback and the insulating effects of snow cover, significantly modulate near-surface air temperature by altering the partitioning of sensible and latent heat fluxes 32, 36 . Part of the context for the research reported here is that it has been suggested that external forcing of the climate system has resulted in background conditions that may be conducive to extreme temperature variations. For example, it has been argued that Arctic Amplification has led to changes in mid-latitude circulation wave patterns that have elevated the risk of whiplash events 18, 34 . Associated sea ice anomalies, particularly the reduction of sea ice in the Barents-Kara Seas, are recognized as important drivers of downstream teleconnection patterns across Eurasia, influence temperature regimes in East Asia 35 . Additionally, tropical sea surface temperature anomalies, such as those associated with ENSO, can indirectly influence mid-latitude temperature variability through their modulation of global-scale atmospheric circulation 36 . Despite previous research the current understanding of the processes responsible for extreme temperature variations continues to have several limitations. Existing work has predominantly focused on individual events, such as isolated cold waves or heatwaves, does not yet provide a systematic, quantitative analysis of the transition process itself. Furthermore, research focused on China remains relatively scarce compared to North America and Europe, with existing studies primarily examining North China. Significant temperature fluctuations that frequently occur during winter in eastern China, particularly in its southern regions by high climate sensitivity, socio-economic vulnerability, and high population density, have not received sufficient attention. This study helps to fill these gaps in two ways. It employs ERA5 reanalysis data from 1979 to 2024 to identify the dominant atmospheric circulation patterns associated with wintertime extreme temperature variation events in eastern China and elucidates their underlying physical mechanisms from both thermodynamic and dynamic perspectives. The findings are expected to provide a scientific basis for future research and inform regional climate adaptation strategies. Results Background of Extreme Temperature Variation One approach for quantifying the influence of abrupt weather change events is through the use of variability indicators 18 . In this study, the variance of daily two-meter temperature anomalies during winter (December, January, February) was selected as the standard for measuring the intensity of extreme temperature variation events. A larger variance indicates the presence of a greater amplitude of temperature fluctuations. As illustrated in Fig. 1 (a), which presents the climatology of winter two-meter daily mean temperature variance in China, a region of high variance between 105°–135°E and 18°–54°N was identified. This area, characterized by generally high standard deviation values, was subsequently defined as the eastern China study region. To facilitate the selection of extreme years for analysis, the long-term climatic background of temperature variability was examined. Figure 1 (b) presents the wintertime two-meter temperature variance in eastern China for the period 1979–2024, calculated from daily mean temperatures. The time series exhibits pronounced interannual fluctuations, with years of above- and below-average variance (red and blue bars respectively) alternating. Notably, the winter of 2023–24 exhibited the highest variance in the entire 45-year time series, indicating that the intensity of extreme temperature variations during this winter was the most pronounced in the 1979–2024 period. This event was therefore selected for detailed analysis. Temperature and precipitation anomalies for the winter of 2023-24 were extracted to examine the sub-seasonal evolution. As shown in Fig. 1 (c), pronounced anomalies in surface air temperature and precipitation were observed. Exceptional temperature shifts are evident, particularly during four specific events: Warm Spell I, Cold Spell I, Cold Spell II, and Warm Spell II. Temperature changes during these events were exceptionally sharp, highlighting the record-breaking temperature whiplash characteristics of this winter. The temperature anomaly contour shows that two marked cold air incursions occurred in mid-to-late December and early February, respectively. During these periods, regional temperature anomalies fell considerably below the climatological mean, with lowest values dropping below − 10°C. Substantial positive temperature anomalies were observed both before and after these two cold air events, demonstrating a pattern of abrupt shifts between extreme cold and warm conditions, including rapid thaws. Precipitation anomalies are typically situated between warm and cold anomalies, occurring after warm phases and before cold phases. This pattern suggests that abrupt temperature shifts may be accompanied by rainfall, which potentially intensify surface cooling via rain-radiation feedback mechanisms. To determine the time scale that best captures the dominant features of temperature variability in eastern China during the 2023–2024 winter, wavelet and global wavelet power spectra of two-meter temperature anomalies were analyzed over the extended cold season (Oct 1, 2023 to April 30, 2024). The wavelet spectrum presented in Fig. 2 reveals high-energy regions (indicated in red and yellow) concentrated in the 10–60 day band. Based on this observation, all data utilized in the subsequent mechanism analyses were subjected to a bandpass filter to isolate variation in the 10–60 day band, thereby enhancing the robustness of the results by focusing on the primary oscillation period. Circulation Analysis: Coupled Upper- and Lower-Level Dynamics The spatial distribution of two-meter temperature anomalies during the four events that have been identified exhibit bimodal patterns of intense cold-warm contrasts (Fig. 3 ). The 850 hPa wind field vectors indicate that the southward advection of cold air masses during cold events (Figs. 3 b, c) was primarily driven by the anomalous strengthening of the Siberian High (defined over 40°–60°N, 80°–120°E), which transitioned into a significant positive anomaly state during these events. In contrast, warm spells (Figs. 3 a, d) were characterized by extreme negative sea level pressure (SLP) anomalies. These SLP shifts altered the low-level wind structure, affecting temperature advection into eastern China. At the upper levels (Fig. 4 ), significant intra-seasonal variability was observed in the East Asian Trough (EAT), particularly through geopotential height and vorticity anomalies over the Bohai Sea region (35°–45°N, 105°–135°E). During cold periods, negative geopotential anomalies at 300 hPa indicated an enhanced upper-level trough, which facilitated the southward penetration of polar air. A T-N wave activity flux analysis reveals that planetary wave disturbances propagated wave energy southeastward from Siberia to East Asia, providing the dynamical forcing for these rapid transitions. Notably, lead-lag correlation analysis confirms that the anomalous strengthening of the Siberian High preceded the development of the East Asian Trough. This suggests a coupled dynamical linkage where low-level high-pressure anomalies modulate transient eddy activity and baroclinicity, which in turn deepens the EAT through vorticity forcing 37 . Thermodynamic Diagnosis: The North-South Dichotomy To identify the physical processes driving these temperature shifts, we also conducted a diagnostic analysis of the vertically integrated (1000–300 hPa) thermodynamic equation (Eq. 1 ), which is repeated here for reference: $$\:{\left(\frac{\partial\:T}{\partial\:t}\right)}^{{\prime\:}}={-\left(u\frac{\partial\:T}{\partial\:x}\right)}^{{\prime\:}}{-\left(v\frac{\partial\:T}{\partial\:y}\right)}^{{\prime\:}}+{\left(\omega\:\sigma\:\right)}^{{\prime\:}}+{\left(\frac{\stackrel{\prime }{Q}}{{C}_{p}}\right)}^{{\prime\:}}$$ 1 . Note that \(\:\dot{Q}\) represents the non-adiabatic (diabatic) heating rate, primarily associated with radiative processes and latent heat release. Figure 5 , which displays maps of the terms of this equation, reveal a pronounced dichotomy between northern and southern eastern China. We note two particularly important subregions with notable thermal characteristics in the northern (38°–50°N, 110°–122°E; outlined in red) and southern (22°–30°N, 103°–117°E; outlined in blue) parts of the domain respectively. Combined with the temporal evolution of the vertically integrated terms of the thermodynamic equation shown in Figs. 6 (a ,b), we find that variations in SAT over the northern subregion were dominated by the non-adiabatic heating term. The magnitude of the diabatic term is greater than that of the temperature advection term, indicating that local radiative feedbacks (such as those mediated by cloud cover) are the primary drivers of rapid temperature change. Lead-lag correlations in Fig. 6 d show that upper-level vorticity anomalies led non-adiabatic heating by approximately 2–6 days, while Fig. 6 c shows that Siberian High anomalies typically preceded vorticity anomalies over the Bohai Sea region. This suggests that the intensification of the lower-level high-pressure system can trigger an upper-level circulation response, thereby establishing a dynamical chain from upper-level circulation to surface thermal response. In contrast, temperature changes in the southern subregion were jointly influenced by meridional temperature advection and non-adiabatic heating. This analysis combined with Fig. 3 indicates that the southward intrusion of cold air masses, guided by the Siberian High, resulted in strong negative advection that significantly contributed to surface cooling. Unlike the north, the lead-lag relationship between vorticity and diabatic heating was not statistically significant in the south (Fig. 6 e), suggesting a more complex confluence of multiple factors. To clearly depict the processes involved in the extreme temperature variations in eastern China during the winter of 2023–2024, a conceptual model was developed (Fig. 7 ). This model synthesizes the findings from the circulation and thermodynamic diagnostics discussed above. It systematically depicts the synergistic effects of the Siberian High and the East Asian Trough—mediated through coupled upper- and lower-level interactions—and elucidates their governing mechanisms for temperature variations across northern and southern eastern China via diabatic heating and temperature advection processes. The arrows in the figure illustrate the pathways of interaction between circulation anomalies and thermodynamic processes, indicating the physical chain of mechanisms described in this section. Long-term Variability and Synergistic Effects Historical analysis for the 1979–2023 period contextualizes the extreme nature of the 2023–2024 winter. As shown in Fig. 8 a, the variance of the Siberian High reached a historical maximum during this winter, confirming its unprecedented variability compared to the 45-year record. Scatter plots shown in Fig. 8 (b, c, d) demonstrate a strong positive correlation (r = 0.52, p \(\:<\) 0.001) between the variability of the Siberian High and the East Asian Trough (Fig. 8 b), further supporting their coupled nature. However, the direct correlation between EAT vorticity variance and SAT variance in eastern China is weak (r = 0.07; Fig. 8 c), whereas the Siberian High shows a more significant link with surface temperature variance (r = 0.34, p \(\:=0.024;\text{F}\text{i}\text{g}\text{u}\text{r}\text{e}\:8\text{d}\) ). This discrepancy points to a nonlinear influence. Figure 9 , which presents a stratified synergy analysis based on years when both systems are in their positive phases, provides some insight into the nature of the non-linear interactions. Given the limited number of samples satisfying the criteria, for intervals with zero sample size, the missing side of the system is estimated using the climatological mean to avoid discarding the combination entirely due to data absence. It indicates that there is a pronounced EAT impact on regional temperature only when the Siberian High variability exceeds specific thresholds (1.0–1.5 standard deviations). This suggests that while the SBH acts as the primary driver, its synergistic interaction with the EAT modulates the magnitude of temperature variations through the distinct thermodynamic pathways illustrated in the conceptual model (Fig. 7 ). Conclusion and Discussion The winter of 2023–2024 in eastern China was characterized by unprecedented temperature variability, marking the most intense extreme temperature whiplash recorded since 1979. We performed a systematic analysis of 45 years of ERA5 reanalysis data to place this event in context and understand the processes involved. Summary of findings The regional surface air temperature anomaly variance reached its historical maximum over the 1979–2024 period in the winter of 2023–2024, featuring a distinct "cold-warm bimodal" pattern. This included at least four marked extreme events during the winter, with the mid-December and early February transitions between events of opposite sign representing the prominent whiplash events. The Siberian High and the East Asian Trough were identified as the principal atmospheric systems driving these variations. Circulation analysis reveals that the anomalous strengthening and rapid phase reversals of the SBH acted as the primary driver, regulating the advection of cold and warm air masses. Lead-lag correlations indicate that SBH anomalies typically preceded the development of EAT vorticity anomalies, suggesting a coupled dynamical chain where the SBH modulates transient eddy activity and upper-level response. Distinct physical processes were found to govern rapid temperature changes across different parts of eastern China, characterized by northern and southern sub-regions. Temperature variations in the northern region were primarily modulated by non-adiabatic (diabatic) heating through SBH-EAT coupled anomalies. In contrast, the temperature variations in the southern region resulted from a confluence of meridional temperature advection driven by northerly airflows associated with the SBH, and diabatic heating processes. Further insight was obtained by considering nonlinear synergistic effects on seasonal fluctuations on surface temperature variability. The analysis of these effects indicates that the Siberian High plays a key role in modulating the effect of EAT variability. The overall linear correlation between EAT variability and SAT variability is weak (r = 0.07), but its influence on regional temperature becomes large when SBH variability reaches high level between 1.0 and 1.5 standard deviations of its climatological level, highlighting that a robust SBH background is requisite for the EAT to effectively modulate temperature extremes. Discussion The extreme temperature fluctuations of the 2023–2024 winter underscore the risks posed by “temperature whiplash” in a changing climate. Some research has suggested that externally forced changes in the Arctic climate, including warming that is enhanced through Arctic Amplification processes and sea ice reduction in the Barents-Kara Seas, has helped to establish conducive background conditions for the occurrence of extreme temperature variations by altering mid-latitude wave patterns 22 , 38 – 40 . While that research provides part of the context for this study, we have focused here only on internal atmospheric dynamical processes, focusing specifically on the winter of 2023–2024. Although we find a predominance of high-variance years after the year 2000 in ERA5 (Fig. 8 a), we have not considered long-term trends in winter surface temperature variability. The episodic nature of these peaks and concern that they may be affected by changes in ERA5 assimilated data sources over time leads us to being caution about the interpretation of the observed surface temperature variance changes. Nevertheless, the large temperature fluctuations in Eastern China that we have studied deserve heightened attention due to the region's high population density and socio-economic vulnerability. Rapid transitions from extreme warmth to extreme cold, accompanied by precipitation can trigger complex rain-radiation feedbacks that intensify surface cooling, leading to severe impacts on agriculture and public health. This study therefore provides a scientific reference for the mechanisms of extreme winter temperature variations in China. Future work should further refine the nonlinear relationships identified here and explore the potential for these mechanisms to intensify under different global warming scenarios to improve regional climate adaptation and risk management. Methods Data The data utilized in this study were obtained from the ERA5 reanalysis dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). As the fifth-generation ECMWF atmospheric reanalysis product, ERA5 is characterized by high temporal and spatial resolution and provides hourly data on global meteorological variables. For this investigation, global daily mean data from ERA5 were utilized, including two-meter temperature (t2m), zonal wind (u), meridional wind (v), vertical velocity (w), and geopotential height (z). The dataset spans the period from 1979 to March 13, 2024, with a spatial resolution of 1°×1°. Except for surface variables, the data are provided on 37 atmospheric levels in the vertical dimension. Data formatting and processing were conducted using the Climate Data Operators (CDO) software. All data were linearly detrended prior to analysis to ensure that the analyses are not affected by secular changes caused by human induced climate change. Definition of Extreme Temperature Variation Extreme temperature variation, also known as "temperature whiplash," is defined as a phenomenon characterized by a rapid and pronounced shift from a sustained temperature condition (e.g., a prolonged cold wave) to its opposite state. Such rapid transitions often precede or follow the breaking of temperature records 41 . Building on previous research 35 , this study defines a "negative extreme temperature variation event" as a transition from an extreme warm period to an extreme cold period occurring within a temporal window of 3 to 7 days. Correspondingly, a "positive extreme temperature variation event" is defined as the transition from an extreme cold period to an extreme warm period. Specifically: A warm period refers to a duration during which the regional average daily surface air temperature anomaly remains positive for more than three consecutive days. It must include at least one warm day where the surface air temperature (SAT) anomaly in the study region is higher than 1s, where s represents the standard deviation of the daily SAT anomalies. (approximately 2.94 K). A cold period is defined analogously, requiring the regional average daily SAT anomaly to remain negative for more than three consecutive days, including at least one cold day where the SAT anomaly is lower than − 1s. Anomalous temperature tendency equation To investigate the key processes of intra-seasonal temperature anomaly propagation, diagnostic analyses of the temperature budget were conducted. The anomalous temperature tendency equation is expressed as 42 : $$\:{\left(\frac{\partial\:T}{\partial\:t}\right)}^{{\prime\:}}={-\left(u\frac{\partial\:T}{\partial\:x}\right)}^{{\prime\:}}{-\left(v\frac{\partial\:T}{\partial\:y}\right)}^{{\prime\:}}+{\left(\omega\:\sigma\:\right)}^{{\prime\:}}+{\left(\frac{\stackrel{\prime }{Q}}{{C}_{p}}\right)}^{{\prime\:}}$$ 1 $$\:\sigma\:=\frac{\alpha\:}{{C}_{p}}-\frac{\partial\:T}{\partial\:p}$$ 2 In this formula, α is the specific density of air; Cp is the specific heat of air at constant pressure, and \(\:\dot{Q}\) represents the non-adiabatic (diabatic) heating rate. Other symbols follow meteorological conventions. T-N wave activity flux The T-N wave activity flux was utilized to describe the propagation and accumulation of wave energy, with the two-dimensional horizontal expression as 43 : where \(\:\stackrel{-}{u}\) and \(\:\stackrel{-}{v}\) represent the climatological states of zonal and meridional wind, ψ′ represents the perturbation of the stream function relative to the climatological state, p is the normalized pressure (p = Pressure/1000 hPa), and |U| is the absolute value of the horizontal wind speed. Definition of synergy We distinguish between the synergistic and antagonistic effects of two climatic factors (e.g., the Siberian High and East Asian Trough) 44 , as follows. We denote the response of a variable y when only the first of two factors is present as y₁, when only the second of two factors is present by y₂, and when both are present simultaneously as y₁₂. A synergistic effect is said to occur when |y₁₂| > max ( |y₁|, |y₂| ), and an antagonistic effect when |y₁₂| < min ( |y₁|, |y₂| ). To quantify interaction strength and direction, we use a linear additive model and define the synergy value as: $$\:Synergy=\:{y}_{12}-{y}_{1}-{y}_{2}+{y}_{0}$$ 4 where \(\:{y}_{0}\) is a baseline (no factor) value. This measures the deviation from additivity: positive synergy means extra enhancement; negative synergy indicates mutual weakening; zero implies a lack of interaction. The metric captures interaction magnitude independent of baseline. Wavelet Analysis and Significance Testing To determine the primary oscillation periods in eastern China during the 2023–2024 winter, the Morlet wavelet analysis method was employed to perform wavelet and power spectrum analyses on the two-meter temperature anomalies. Statistical tests performed in the course of the analysis account for the effects of serial correlation through the use of effective numbers of degrees of freedom 45 – 48 . Declarations Competing interests The authors declare no competing interests. Author Contribution Conceptualization, J.L., B.L. and G.H.; methodology, J.L., and B.L.; data curation, G.H. and J.L.; visualization, J.L.; formal analysis, J.L., and B.L.; writing—original draft preparation, J.L.; writing—review and editing, B.L., F.W.Z. and J.J.L. Acknowledgements This work was supported by the State Key Laboratory of Climate System Prediction and Risk Management (CPRM) initiative project (Grant No. CPRM-2025¬NUIST-012); Sichuan Science and Technology Program(No.2025YFNH0006); Water Conservancy Science and Technology Project of Jiangsu Province (No. 2025017);the funding of Fengyun Application Pioneering Project (FY- APP); the Bohai Rim Regional Meteorological Science and Technology Collaborative Innovation Fund project(QYXM202409). Data availability The ERA5 reanalysis dataset is available at https://cds.climate.copernicus.eu Code availability The source codes in this study are available to qualified researchers on reasonable request from the corresponding author. References Seneviratne, S., et al., IPCC AR6 WGI Chap. 11: Weather and Climate Extreme Events in a Changing Climate. 2021. 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Nakamura, A formulation of a phase-independent wave-activity flux for stationary and migratory quasigeostrophic eddies on a zonally varying basic flow[J]. Journal of the Atmospheric Sciences, 2001. 58(6): p. 608–627. Wang, H., et al., The synergistic effect of the preceding winter mid-latitude North Atlantic and summer tropical eastern Indian Ocean SST on summer extreme heat events in northern China[J]. Weather and Climate Extremes, 2024. 44: p. 100660. Bretherton, C.S., et al., The Effective Number of Spatial Degrees of Freedom of a Time-Varying Field[J]. Journal of Climate, 1999. 12(7): p. 1990–2009. Huang, J., Method of Meteorological Statistic Analysis and Forecasting. 2004, China Meteorological Press: Beijing, China. Wei, F., Modern Climate Statistical Diagnosis and Prediction Techniques, 2nd ed.[M]. 2007. Wu, H. and L. Wu, Diagnosis and Prediction Method of Climate Variability. 2005, China Meteorological Press: Beijing, China. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9413098","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":633171563,"identity":"25853487-c0fe-438f-8f23-bdc9db9a90b4","order_by":0,"name":"Junru Li","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Junru","middleName":"","lastName":"Li","suffix":""},{"id":633171568,"identity":"49923b3f-c1da-4021-b59a-ece7977bf61e","order_by":1,"name":"Boqi Liu","email":"","orcid":"","institution":"China Meteorological Administration","correspondingAuthor":false,"prefix":"","firstName":"Boqi","middleName":"","lastName":"Liu","suffix":""},{"id":633171569,"identity":"630edc40-b809-48a1-a09e-b6070cfed0e5","order_by":2,"name":"Francis W. Zwiers","email":"","orcid":"","institution":"University of Victoria","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"W.","lastName":"Zwiers","suffix":""},{"id":633171574,"identity":"a03e565c-fcaf-4cd1-aa73-4ac147124663","order_by":3,"name":"Guangxin He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYDACCQbGByASwiZSC7MBiOQhRQsbWCXxWuSjm49VF7ZZ2NszMB+8zcNgl0dQi+GdY2m3Z7ZJJPYwsCVb8zAkFxPWMiPH7DZvm0QCDwOPmTQPw4HEBsJa8r8VA7XY8zDwfyNOi7xEDhszUAtjDwMPG3FaDCTSjKV5zgH9cpjN2HKOQTIRtsxIfviZp6zOnr29+eGNNxV2RNhyAMZiBnMJqQfZQtDQUTAKRsEoGAUAhoIv8Klk5bgAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Guangxin","middleName":"","lastName":"He","suffix":""},{"id":633171578,"identity":"36de21f4-1e16-40a8-9319-dd8eb137f20c","order_by":4,"name":"Jingjia Luo","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jingjia","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2026-04-14 09:09:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9413098/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9413098/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108681119,"identity":"3c8311b0-45a1-47cb-a771-7b45e5077b64","added_by":"auto","created_at":"2026-05-07 09:21:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":507507,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEastern China exhibited high variance in winter temperature anomalies during 2023-2024.\u003c/strong\u003e (a) Variance (unit: K\u003csup\u003e2\u003c/sup\u003e) of winter two-meter air temperature in China for the period 1994–2023. (b) Regional winter seasonal temperature variance from 1979-2023 (bars, unit: K\u003csup\u003e2\u003c/sup\u003e, green dashed line represents the average variance, red bars for values greater than average, blue bars for values less than average) and regional winter seasonal average temperature anomaly (line graph, unit: K). (c) Regional winter seasonal temperature anomaly for 2023-2024 (contour lines, unit: K, red solid lines for positive anomalies, black solid lines for 0 values, blue dashed lines for negative values) and regional winter seasonal precipitation anomaly (shading, unit: mm).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9413098/v1/deccd045919d4c4f11dd8226.png"},{"id":108681125,"identity":"606bfef4-9bf3-4ed4-8be1-8d97c96a9202","added_by":"auto","created_at":"2026-05-07 09:21:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":75827,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOscillation periods of 10 to 60 days dominate temperature variability in eastern China. \u003c/strong\u003e\u0026nbsp;Wavelet power spectrum of two-meter temperature anomalies in the eastern region of China during the 2023-2024 winter (dots indicate parts of the spectrum that are significantly greater than expected under a red-noise hypothesis at the 5% significance level) and global wavelet spectra (black solid line is the power spectrum of two-meter temperature anomalies, red dashed line are critical values for evaluating the estimated spectrum against a red-noise hypothesis at the 5% significance level).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9413098/v1/6ab5444fdc5f2a5a56d44bc6.png"},{"id":108681121,"identity":"5f924fd0-b9b0-454f-9b65-ff53da5beec1","added_by":"auto","created_at":"2026-05-07 09:21:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1222268,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSea-level pressure anomalies reveal rapid phase reversals of the Siberian High.\u003c/strong\u003e (a-d)Sea-level pressure anomalies (shading, unit: hPa; dotted areas are statistically significant from zero at the 5% level) and 850hPa wind field (black vectors, unit: m/s) for the four temperature events in the 2023-2024 winter\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9413098/v1/90a3463e176784a5b8c217f0.png"},{"id":108681120,"identity":"dec829d7-d1a5-45a1-890e-357bab576d44","added_by":"auto","created_at":"2026-05-07 09:21:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1280746,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUpper-level circulation anomalies modulate wave energy propagation over East Asia\u003c/strong\u003e (a-d)300hPa geopotential height anomalies (shading, unit: gpm; dotted areas are statistically significant from zero at the 5% level) and wind field (black vectors, unit: m/s) and T-N wave activity flux (purple vectors, unit: m²/s²) for the four temperature events in the 2023-2024 winter\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9413098/v1/c598cd623b17ff6b7e6612c9.png"},{"id":108805401,"identity":"8db8da97-0d04-4d94-8b6d-e1c27071d669","added_by":"auto","created_at":"2026-05-08 15:25:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1106303,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMeridional temperature advection and diabatic heating dominate the thermodynamic forcing of temperature anomalies. \u003c/strong\u003e\u0026nbsp;Spatial distribution of vertically integrated (1000-300 hPa) terms in the thermodynamic equation during different winter periods in eastern China (shading, unit: K/day) and two-meter temperature anomalies (contour lines, unit: K) and horizontal wind field anomalies (vectors, unit: m/s, with temperature advection term overlaid on 850hPa wind field, adiabatic term overlaid on 500hPa wind field, non-adiabatic term overlaid on 300hPa wind field)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9413098/v1/64985e31f8c60278f62bf417.png"},{"id":108806159,"identity":"5eb18b9d-19e5-4f9b-bed0-1e25d79d12e7","added_by":"auto","created_at":"2026-05-08 15:27:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":316233,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThere is a thermal contrast between key northern and southern regions; lead-lag correlation analysis reveals the dynamical pathway from circulation to diabatic heating.\u003c/strong\u003e (a) Vertically integrated (1000-300 hPa) terms of the thermodynamic equation during the 2023-2024 winter in the northern key zone of eastern China (38-50°N, 110-122°E) (line graph, with different colored lines representing different terms in the equation) and 300 hPa Bohai Sea region vorticity anomaly (black line) and Siberian high anomaly (purple line); (b) Vertically integrated (1000-300 hPa) terms of the thermodynamic equation during the 2023-2024 winter in the southern key zone of eastern China (22-30°N, 103-117°E) (line graph, with different colored lines representing different terms in the equation) and 300 hPa Bohai Sea region vorticity anomaly (black line) and Siberian high anomaly (purple line); (c) Lead-lag correlation coefficients between 300 hPa Bohai Sea region vorticity anomaly and Siberian high anomaly (red dashed line indicates the 5% significance level; positive days indicate that the Siberian high anomaly leads the Bohai Sea region vorticity anomaly while negative days mean that it lags); (d) Lead-lag correlation coefficients between 300 hPa Bohai Sea region vorticity anomaly and non-adiabatic heating in the northern key zone of eastern China (38-50°N, 110-122°E) (red dashed line indicates 5% significance level; positive days mean the Bohai Sea region vorticity anomaly leads non-adiabatic heating and vice-versa for negative days); (e) Lead-lag correlation coefficients between 300 hPa Bohai Sea region vorticity anomaly and non-adiabatic heating in the southern key zone of eastern China (22-30°N, 103-117°E) (red dashed line indicates 5% significance level; positive days mean the Bohai Sea region vorticity anomaly leads non-adiabatic heating, and vice-versa for negative days).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9413098/v1/8e30075885266ff78a0ecfe7.png"},{"id":108805891,"identity":"e0858678-f529-4b16-bb2c-2317016524fb","added_by":"auto","created_at":"2026-05-08 15:27:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":173770,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA conceptual model illustrates the coupled dynamics of extreme temperature variation.\u003c/strong\u003e Schematic diagram of the extreme temperature variation mechanism process in the 2023-2024 winter. Changes in the Siberian High (SBH) precede the East Asian Trough (EAT), which is further enhanced through increased Rossby wave activity. Subsequently, the two systems synergistically drive temperature advection and diabatic heating, regulating extreme temperature variations.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9413098/v1/bdc1a88a8de9ec8eefa6dc0b.png"},{"id":108806788,"identity":"a00054d6-0780-4de4-981e-b68afe242c30","added_by":"auto","created_at":"2026-05-08 15:29:28","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":321084,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLong-term trends show increasing variability in key circulation systems.\u003c/strong\u003e (a) Winter 300 hPa vorticity variance in the Bohai Sea region (35-45°N, 105-135°E) from 1979-2023 (blue line graph, dashed line represents linear trend); Siberian high variance (40-60°N, 80-120°E) (red line graph, dashed line represents linear trend); (b) Scatter plot of 300 hPa vorticity variance anomalies in the Bohai Sea region (35-45°N, 105-135°E) and Siberian high variance anomalies (40-60°N, 80-120°E) during winters from 1979-2023; (c) Scatter plot of eastern China t2m variance anomalies and Bohai Sea region 300 hPa vorticity variance anomalies; (d) Scatter plot of eastern China t2m variance anomalies and Siberian high variance anomalies (dashed line is the linear regression line, red dot is the 2023-24 winter).\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9413098/v1/f896f03230eb23e746d74b95.png"},{"id":108681124,"identity":"a23eccad-dba6-437d-b9a9-b2ca5bec0b0e","added_by":"auto","created_at":"2026-05-07 09:21:46","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":75270,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSynergistic effects between circulation systems modulate temperature variability.\u003c/strong\u003eHeat map of synergy analysis between 300 hPa vorticity variance in the Bohai Sea region and Siberian High variance under different standard deviations (the three levels are defined based on standardized anomalies, with units of standard deviation multiples, which are dimensionless. numbers in parentheses are sample sizes)\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9413098/v1/62c97b7c8d231e5be0b16e4d.png"},{"id":109129725,"identity":"615d2049-54f4-48e6-a812-b328cd4e88e4","added_by":"auto","created_at":"2026-05-12 20:25:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4933886,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9413098/v1/b4527b26-c018-4086-9a5c-5e25044aa41a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Siberian High variability shaped the record-breaking winter extreme temperature variation over eastern China in 2023–2024","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the Intergovernmental Panel on Climate Change (IPCC), global climate change is significantly increasing the frequency and intensity of extreme weather and climate events \u003csup\u003e1\u003c/sup\u003e. The increasing in frequency in record breaking extreme events \u003csup\u003e2-8\u003c/sup\u003e, has garnered growing attention from both the scientific community and risk management sectors. The intensity or duration of these extremes frequently exceeds historical thresholds \u003csup\u003e9-12\u003c/sup\u003e, leading to impacts on ecosystems and human societies that surpass those caused by normal climatic fluctuations. Existing research has largely concentrated on individual extreme events \u003csup\u003e13\u003c/sup\u003e, frequently overlooking the rapid changes that often precede them . Also, extremes frequently often occur in compound forms, characterized by multiple events happening simultaneously or in rapid succession \u003csup\u003e14\u003c/sup\u003e. A particular form of compound event involves an abrupt reversal of conditions from one extreme to another, which is exemplified by rapid shifts in temperature \u003csup\u003e15\u003c/sup\u003e, precipitation \u003csup\u003e16, 17\u003c/sup\u003e, and atmospheric circulation \u003csup\u003e18, 19\u003c/sup\u003e. Amongst these, the rapid shift from extreme warmth to extreme cold, or vice versa, is specifically termed \u0026quot;temperature whiplash\u0026quot; and is defined as an extreme temperature variation event.\u003c/p\u003e\n\u003cp\u003eHigh-impact temperature whiplash events have been recorded multiple times worldwide in recent years. Two, of many, notable examples include the abrupt cooling in North America during spring 2012 following an unusually warm period, which damaged crops \u003csup\u003e20\u003c/sup\u003e, and a 24-hour shift from a heatwave to a blizzard in the Rocky Mountains in 2020 \u003csup\u003e21\u003c/sup\u003e. Such cases underscore the need to better understand the physical processes governing rapid transitions between temperature extremes. Previous studies have suggested that global climate change exerts a significant influence on regional weather whiplash events, primarily through the modulation of large-scale atmospheric circulations \u003csup\u003e22-28\u003c/sup\u003e. Consequently, the identification of intrinsic atmospheric circulation modes associated with weather whiplash is important for assessing the potential impacts of global climate change on these phenomena.\u003c/p\u003e\n\u003cp\u003eExtreme temperature variation is defined by pronounced shifts between anomalously low and high temperature conditions. Research has shown that these\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eevents occur most frequently in mid-latitude regions, such as East Asia and eastern North America, where they are often more intense and exhibit shorter transition times than elsewhere. This phenomenon is primarily driven by enhanced atmospheric Rossby wave activity and jet stream meandering \u003csup\u003e29, 30\u003c/sup\u003e. For instance, the southeastward propagation of Rossby wave energy from source regions like the Kara Sea or Northern Europe can trigger wave trains characterized by alternating high- and low-pressure anomalies that induce sharp temperature reversals \u003csup\u003e31, 32\u003c/sup\u003e. Persistent blocking highs, such as Ural and Okhotsk blocking, play a central role in guiding cold air southward, thereby facilitating transitions between temperature states \u003csup\u003e33\u003c/sup\u003e. From a broader perspective, extreme temperature whiplash can be conceptualized as a rapid transition between persistent large-scale circulation patterns, such as abrupt alternation between distinct 500 hPa geopotential height anomalies \u003csup\u003e18\u003c/sup\u003e. Furthermore, changes in the position and intensity of the mid-latitude jet stream, coupled with shifts in storm tracks, have also been identified as key determinants of the frequency and intensity of extreme variations \u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eUnderlying the large-scale circulation background, local physical processes serve to amplify or buffer near-surface temperature changes. Temperature advection constitutes a core process; in winter, warm-to-cold whiplash events are primarily driven by strong cold advection associated with anomalous northerly winds \u003csup\u003e35\u003c/sup\u003e. Cloud cover and associated radiative feedbacks also play a critical role. During a transition, increased cloud cover can promote cooling by attenuating incoming solar shortwave radiation; conversely, decreased cloud cover can facilitate warming by enhancing solar insolation \u003csup\u003e32\u003c/sup\u003e. It should be noted that clouds also trap outgoing longwave radiation, which may buffer cooling, a complex feedback mechanism that requires careful diagnostic analysis. Furthermore, land-atmosphere interactions, such as soil moisture-atmosphere feedback and the insulating effects of snow cover, significantly modulate near-surface air temperature by altering the partitioning of sensible and latent heat fluxes \u003csup\u003e32, 36\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePart of the context for the research reported here is that it has been suggested that external forcing of the climate system has resulted in background conditions that may be conducive to extreme temperature variations. For example, it has been argued that Arctic Amplification has led to changes in mid-latitude circulation wave patterns that have elevated the risk of whiplash events \u003csup\u003e18, 34\u003c/sup\u003e. Associated sea ice anomalies, particularly the reduction of sea ice in the Barents-Kara Seas, are recognized as important drivers of downstream teleconnection patterns across Eurasia, influence temperature regimes in East Asia \u003csup\u003e35\u003c/sup\u003e. Additionally, tropical sea surface temperature anomalies, such as those associated with ENSO, can indirectly influence mid-latitude temperature variability through their modulation of global-scale atmospheric circulation \u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDespite previous research the current understanding of the processes responsible for extreme temperature variations continues to have several limitations. Existing work has predominantly focused on individual events, such as isolated cold waves or heatwaves, does not yet provide a systematic, quantitative analysis of the transition process itself. Furthermore, research focused on China remains relatively scarce compared to North America and Europe, with existing studies primarily examining North China. Significant temperature fluctuations that frequently occur during winter in eastern China, particularly in its southern regions by high climate sensitivity, socio-economic vulnerability, and high population density, have not received sufficient attention. This study helps to fill these gaps in two ways. It employs ERA5 reanalysis data from 1979 to 2024 to identify the dominant atmospheric circulation patterns associated with wintertime extreme temperature variation events in eastern China and elucidates their underlying physical mechanisms from both thermodynamic and dynamic perspectives. The findings are expected to provide a scientific basis for future research and inform regional climate adaptation strategies.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eBackground of Extreme Temperature Variation\u003c/h2\u003e \u003cp\u003eOne approach for quantifying the influence of abrupt weather change events is through the use of variability indicators \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In this study, the variance of daily two-meter temperature anomalies during winter (December, January, February) was selected as the standard for measuring the intensity of extreme temperature variation events. A larger variance indicates the presence of a greater amplitude of temperature fluctuations. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(a), which presents the climatology of winter two-meter daily mean temperature variance in China, a region of high variance between 105\u0026deg;\u0026ndash;135\u0026deg;E and 18\u0026deg;\u0026ndash;54\u0026deg;N was identified. This area, characterized by generally high standard deviation values, was subsequently defined as the eastern China study region.\u003c/p\u003e \u003cp\u003eTo facilitate the selection of extreme years for analysis, the long-term climatic background of temperature variability was examined. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(b) presents the wintertime two-meter temperature variance in eastern China for the period 1979\u0026ndash;2024, calculated from daily mean temperatures. The time series exhibits pronounced interannual fluctuations, with years of above- and below-average variance (red and blue bars respectively) alternating. Notably, the winter of 2023\u0026ndash;24 exhibited the highest variance in the entire 45-year time series, indicating that the intensity of extreme temperature variations during this winter was the most pronounced in the 1979\u0026ndash;2024 period. This event was therefore selected for detailed analysis.\u003c/p\u003e \u003cp\u003eTemperature and precipitation anomalies for the winter of 2023-24 were extracted to examine the sub-seasonal evolution. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(c), pronounced anomalies in surface air temperature and precipitation were observed. Exceptional temperature shifts are evident, particularly during four specific events: Warm Spell I, Cold Spell I, Cold Spell II, and Warm Spell II. Temperature changes during these events were exceptionally sharp, highlighting the record-breaking temperature whiplash characteristics of this winter. The temperature anomaly contour shows that two marked cold air incursions occurred in mid-to-late December and early February, respectively. During these periods, regional temperature anomalies fell considerably below the climatological mean, with lowest values dropping below \u0026minus;\u0026thinsp;10\u0026deg;C.\u003c/p\u003e \u003cp\u003eSubstantial positive temperature anomalies were observed both before and after these two cold air events, demonstrating a pattern of abrupt shifts between extreme cold and warm conditions, including rapid thaws. Precipitation anomalies are typically situated between warm and cold anomalies, occurring after warm phases and before cold phases. This pattern suggests that abrupt temperature shifts may be accompanied by rainfall, which potentially intensify surface cooling via rain-radiation feedback mechanisms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo determine the time scale that best captures the dominant features of temperature variability in eastern China during the 2023\u0026ndash;2024 winter, wavelet and global wavelet power spectra of two-meter temperature anomalies were analyzed over the extended cold season (Oct 1, 2023 to April 30, 2024). The wavelet spectrum presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reveals high-energy regions (indicated in red and yellow) concentrated in the 10\u0026ndash;60 day band. Based on this observation, all data utilized in the subsequent mechanism analyses were subjected to a bandpass filter to isolate variation in the 10\u0026ndash;60 day band, thereby enhancing the robustness of the results by focusing on the primary oscillation period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCirculation Analysis: Coupled Upper- and Lower-Level Dynamics\u003c/h2\u003e \u003cp\u003eThe spatial distribution of two-meter temperature anomalies during the four events that have been identified exhibit bimodal patterns of intense cold-warm contrasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The 850 hPa wind field vectors indicate that the southward advection of cold air masses during cold events (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, c) was primarily driven by the anomalous strengthening of the Siberian High (defined over 40\u0026deg;\u0026ndash;60\u0026deg;N, 80\u0026deg;\u0026ndash;120\u0026deg;E), which transitioned into a significant positive anomaly state during these events. In contrast, warm spells (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, d) were characterized by extreme negative sea level pressure (SLP) anomalies. These SLP shifts altered the low-level wind structure, affecting temperature advection into eastern China.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the upper levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), significant intra-seasonal variability was observed in the East Asian Trough (EAT), particularly through geopotential height and vorticity anomalies over the Bohai Sea region (35\u0026deg;\u0026ndash;45\u0026deg;N, 105\u0026deg;\u0026ndash;135\u0026deg;E). During cold periods, negative geopotential anomalies at 300 hPa indicated an enhanced upper-level trough, which facilitated the southward penetration of polar air. A T-N wave activity flux analysis reveals that planetary wave disturbances propagated wave energy southeastward from Siberia to East Asia, providing the dynamical forcing for these rapid transitions. Notably, lead-lag correlation analysis confirms that the anomalous strengthening of the Siberian High preceded the development of the East Asian Trough. This suggests a coupled dynamical linkage where low-level high-pressure anomalies modulate transient eddy activity and baroclinicity, which in turn deepens the EAT through vorticity forcing \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThermodynamic Diagnosis: The North-South Dichotomy\u003c/h3\u003e\n\u003cp\u003eTo identify the physical processes driving these temperature shifts, we also conducted a diagnostic analysis of the vertically integrated (1000\u0026ndash;300 hPa) thermodynamic equation (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which is repeated here for reference:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{\\left(\\frac{\\partial\\:T}{\\partial\\:t}\\right)}^{{\\prime\\:}}={-\\left(u\\frac{\\partial\\:T}{\\partial\\:x}\\right)}^{{\\prime\\:}}{-\\left(v\\frac{\\partial\\:T}{\\partial\\:y}\\right)}^{{\\prime\\:}}+{\\left(\\omega\\:\\sigma\\:\\right)}^{{\\prime\\:}}+{\\left(\\frac{\\stackrel{\\prime }{Q}}{{C}_{p}}\\right)}^{{\\prime\\:}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e.\u003c/p\u003e \u003cp\u003eNote that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\dot{Q}\\)\u003c/span\u003e\u003c/span\u003e represents the non-adiabatic (diabatic) heating rate, primarily associated with radiative processes and latent heat release. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, which displays maps of the terms of this equation, reveal a pronounced dichotomy between northern and southern eastern China. We note two particularly important subregions with notable thermal characteristics in the northern (38\u0026deg;\u0026ndash;50\u0026deg;N, 110\u0026deg;\u0026ndash;122\u0026deg;E; outlined in red) and southern (22\u0026deg;\u0026ndash;30\u0026deg;N, 103\u0026deg;\u0026ndash;117\u0026deg;E; outlined in blue) parts of the domain respectively.\u003c/p\u003e \u003cp\u003eCombined with the temporal evolution of the vertically integrated terms of the thermodynamic equation shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(a ,b), we find that variations in SAT over the northern subregion were dominated by the non-adiabatic heating term. The magnitude of the diabatic term is greater than that of the temperature advection term, indicating that local radiative feedbacks (such as those mediated by cloud cover) are the primary drivers of rapid temperature change. Lead-lag correlations in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed show that upper-level vorticity anomalies led non-adiabatic heating by approximately 2\u0026ndash;6 days, while Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec shows that Siberian High anomalies typically preceded vorticity anomalies over the Bohai Sea region. This suggests that the intensification of the lower-level high-pressure system can trigger an upper-level circulation response, thereby establishing a dynamical chain from upper-level circulation to surface thermal response. In contrast, temperature changes in the southern subregion were jointly influenced by meridional temperature advection and non-adiabatic heating. This analysis combined with Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicates that the southward intrusion of cold air masses, guided by the Siberian High, resulted in strong negative advection that significantly contributed to surface cooling. Unlike the north, the lead-lag relationship between vorticity and diabatic heating was not statistically significant in the south (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee), suggesting a more complex confluence of multiple factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo clearly depict the processes involved in the extreme temperature variations in eastern China during the winter of 2023\u0026ndash;2024, a conceptual model was developed (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). This model synthesizes the findings from the circulation and thermodynamic diagnostics discussed above. It systematically depicts the synergistic effects of the Siberian High and the East Asian Trough\u0026mdash;mediated through coupled upper- and lower-level interactions\u0026mdash;and elucidates their governing mechanisms for temperature variations across northern and southern eastern China via diabatic heating and temperature advection processes. The arrows in the figure illustrate the pathways of interaction between circulation anomalies and thermodynamic processes, indicating the physical chain of mechanisms described in this section.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eLong-term Variability and Synergistic Effects\u003c/h3\u003e\n\u003cp\u003eHistorical analysis for the 1979\u0026ndash;2023 period contextualizes the extreme nature of the 2023\u0026ndash;2024 winter. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea, the variance of the Siberian High reached a historical maximum during this winter, confirming its unprecedented variability compared to the 45-year record. Scatter plots shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e(b, c, d) demonstrate a strong positive correlation (r\u0026thinsp;=\u0026thinsp;0.52, p\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e0.001) between the variability of the Siberian High and the East Asian Trough (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb), further supporting their coupled nature. However, the direct correlation between EAT vorticity variance and SAT variance in eastern China is weak (r\u0026thinsp;=\u0026thinsp;0.07; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec), whereas the Siberian High shows a more significant link with surface temperature variance (r\u0026thinsp;=\u0026thinsp;0.34, p\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:=0.024;\\text{F}\\text{i}\\text{g}\\text{u}\\text{r}\\text{e}\\:8\\text{d}\\)\u003c/span\u003e\u003c/span\u003e). This discrepancy points to a nonlinear influence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, which presents a stratified synergy analysis based on years when both systems are in their positive phases, provides some insight into the nature of the non-linear interactions. Given the limited number of samples satisfying the criteria, for intervals with zero sample size, the missing side of the system is estimated using the climatological mean to avoid discarding the combination entirely due to data absence. It indicates that there is a pronounced EAT impact on regional temperature only when the Siberian High variability exceeds specific thresholds (1.0\u0026ndash;1.5 standard deviations). This suggests that while the SBH acts as the primary driver, its synergistic interaction with the EAT modulates the magnitude of temperature variations through the distinct thermodynamic pathways illustrated in the conceptual model (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Conclusion and Discussion","content":"\u003cp\u003eThe winter of 2023\u0026ndash;2024 in eastern China was characterized by unprecedented temperature variability, marking the most intense extreme temperature whiplash recorded since 1979. We performed a systematic analysis of 45 years of ERA5 reanalysis data to place this event in context and understand the processes involved.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSummary of findings\u003c/h2\u003e \u003cp\u003eThe regional surface air temperature anomaly variance reached its historical maximum over the 1979\u0026ndash;2024 period in the winter of 2023\u0026ndash;2024, featuring a distinct \"cold-warm bimodal\" pattern. This included at least four marked extreme events during the winter, with the mid-December and early February transitions between events of opposite sign representing the prominent whiplash events.\u003c/p\u003e \u003cp\u003eThe Siberian High and the East Asian Trough were identified as the principal atmospheric systems driving these variations. Circulation analysis reveals that the anomalous strengthening and rapid phase reversals of the SBH acted as the primary driver, regulating the advection of cold and warm air masses. Lead-lag correlations indicate that SBH anomalies typically preceded the development of EAT vorticity anomalies, suggesting a coupled dynamical chain where the SBH modulates transient eddy activity and upper-level response.\u003c/p\u003e \u003cp\u003eDistinct physical processes were found to govern rapid temperature changes across different parts of eastern China, characterized by northern and southern sub-regions. Temperature variations in the northern region were primarily modulated by non-adiabatic (diabatic) heating through SBH-EAT coupled anomalies. In contrast, the temperature variations in the southern region resulted from a confluence of meridional temperature advection driven by northerly airflows associated with the SBH, and diabatic heating processes.\u003c/p\u003e \u003cp\u003eFurther insight was obtained by considering nonlinear synergistic effects on seasonal fluctuations on surface temperature variability. The analysis of these effects indicates that the Siberian High plays a key role in modulating the effect of EAT variability. The overall linear correlation between EAT variability and SAT variability is weak (r\u0026thinsp;=\u0026thinsp;0.07), but its influence on regional temperature becomes large when SBH variability reaches high level between 1.0 and 1.5 standard deviations of its climatological level, highlighting that a robust SBH background is requisite for the EAT to effectively modulate temperature extremes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe extreme temperature fluctuations of the 2023\u0026ndash;2024 winter underscore the risks posed by \u0026ldquo;temperature whiplash\u0026rdquo; in a changing climate. Some research has suggested that externally forced changes in the Arctic climate, including warming that is enhanced through Arctic Amplification processes and sea ice reduction in the Barents-Kara Seas, has helped to establish conducive background conditions for the occurrence of extreme temperature variations by altering mid-latitude wave patterns\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. While that research provides part of the context for this study, we have focused here only on internal atmospheric dynamical processes, focusing specifically on the winter of 2023\u0026ndash;2024. Although we find a predominance of high-variance years after the year 2000 in ERA5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea), we have not considered long-term trends in winter surface temperature variability. The episodic nature of these peaks and concern that they may be affected by changes in ERA5 assimilated data sources over time leads us to being caution about the interpretation of the observed surface temperature variance changes.\u003c/p\u003e \u003cp\u003eNevertheless, the large temperature fluctuations in Eastern China that we have studied deserve heightened attention due to the region's high population density and socio-economic vulnerability. Rapid transitions from extreme warmth to extreme cold, accompanied by precipitation can trigger complex rain-radiation feedbacks that intensify surface cooling, leading to severe impacts on agriculture and public health. This study therefore provides a scientific reference for the mechanisms of extreme winter temperature variations in China. Future work should further refine the nonlinear relationships identified here and explore the potential for these mechanisms to intensify under different global warming scenarios to improve regional climate adaptation and risk management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003eThe data utilized in this study were obtained from the ERA5 reanalysis dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). As the fifth-generation ECMWF atmospheric reanalysis product, ERA5 is characterized by high temporal and spatial resolution and provides hourly data on global meteorological variables. For this investigation, global daily mean data from ERA5 were utilized, including two-meter temperature (t2m), zonal wind (u), meridional wind (v), vertical velocity (w), and geopotential height (z). The dataset spans the period from 1979 to March 13, 2024, with a spatial resolution of 1\u0026deg;\u0026times;1\u0026deg;. Except for surface variables, the data are provided on 37 atmospheric levels in the vertical dimension. Data formatting and processing were conducted using the Climate Data Operators (CDO) software. All data were linearly detrended prior to analysis to ensure that the analyses are not affected by secular changes caused by human induced climate change.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of Extreme Temperature Variation\u003c/h2\u003e \u003cp\u003eExtreme temperature variation, also known as \"temperature whiplash,\" is defined as a phenomenon characterized by a rapid and pronounced shift from a sustained temperature condition (e.g., a prolonged cold wave) to its opposite state. Such rapid transitions often precede or follow the breaking of temperature records \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Building on previous research \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, this study defines a \"negative extreme temperature variation event\" as a transition from an extreme warm period to an extreme cold period occurring within a temporal window of 3 to 7 days. Correspondingly, a \"positive extreme temperature variation event\" is defined as the transition from an extreme cold period to an extreme warm period. Specifically:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eA warm period refers to a duration during which the regional average daily surface air temperature anomaly remains positive for more than three consecutive days. It must include at least one warm day where the surface air temperature (SAT) anomaly in the study region is higher than 1s, where s represents the standard deviation of the daily SAT anomalies. (approximately 2.94 K).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eA cold period is defined analogously, requiring the regional average daily SAT anomaly to remain negative for more than three consecutive days, including at least one cold day where the SAT anomaly is lower than \u0026minus;\u0026thinsp;1s.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAnomalous temperature tendency equation\u003c/h2\u003e \u003cp\u003eTo investigate the key processes of intra-seasonal temperature anomaly propagation, diagnostic analyses of the temperature budget were conducted. The anomalous temperature tendency equation is expressed as \u003csup\u003e42\u003c/sup\u003e:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{\\left(\\frac{\\partial\\:T}{\\partial\\:t}\\right)}^{{\\prime\\:}}={-\\left(u\\frac{\\partial\\:T}{\\partial\\:x}\\right)}^{{\\prime\\:}}{-\\left(v\\frac{\\partial\\:T}{\\partial\\:y}\\right)}^{{\\prime\\:}}+{\\left(\\omega\\:\\sigma\\:\\right)}^{{\\prime\\:}}+{\\left(\\frac{\\stackrel{\\prime }{Q}}{{C}_{p}}\\right)}^{{\\prime\\:}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:\\sigma\\:=\\frac{\\alpha\\:}{{C}_{p}}-\\frac{\\partial\\:T}{\\partial\\:p}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this formula, α is the specific density of air; Cp is the specific heat of air at constant pressure, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\dot{Q}\\)\u003c/span\u003e\u003c/span\u003e represents the non-adiabatic (diabatic) heating rate. Other symbols follow meteorological conventions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eT-N wave activity flux\u003c/h2\u003e \u003cp\u003eThe T-N wave activity flux was utilized to describe the propagation and accumulation of wave energy, with the two-dimensional horizontal expression as \u003csup\u003e43\u003c/sup\u003e:\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"678\" height=\"88\"\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{u}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{v}\\)\u003c/span\u003e\u003c/span\u003e represent the climatological states of zonal and meridional wind, ψ\u0026prime; represents the perturbation of the stream function relative to the climatological state, p is the normalized pressure (p\u0026thinsp;=\u0026thinsp;Pressure/1000 hPa), and |U| is the absolute value of the horizontal wind speed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of synergy\u003c/h2\u003e \u003cp\u003eWe distinguish between the synergistic and antagonistic effects of two climatic factors (e.g., the Siberian High and East Asian Trough) \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, as follows. We denote the response of a variable y when only the first of two factors is present as y₁, when only the second of two factors is present by y₂, and when both are present simultaneously as y₁₂. A \u003cem\u003esynergistic effect\u003c/em\u003e is said to occur when \u003cem\u003e|y₁₂| \u0026gt; max\u003c/em\u003e(\u003cem\u003e|y₁|, |y₂|\u003c/em\u003e), and an \u003cem\u003eantagonistic effect\u003c/em\u003e when \u003cem\u003e|y₁₂| \u0026lt; min\u003c/em\u003e(\u003cem\u003e|y₁|, |y₂|\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eTo quantify interaction strength and direction, we use a linear additive model and define the synergy value as:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:Synergy=\\:{y}_{12}-{y}_{1}-{y}_{2}+{y}_{0}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{0}\\)\u003c/span\u003e\u003c/span\u003e is a baseline (no factor) value. This measures the deviation from additivity: positive synergy means extra enhancement; negative synergy indicates mutual weakening; zero implies a lack of interaction. The metric captures interaction magnitude independent of baseline.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eWavelet Analysis and Significance Testing\u003c/h2\u003e \u003cp\u003eTo determine the primary oscillation periods in eastern China during the 2023\u0026ndash;2024 winter, the Morlet wavelet analysis method was employed to perform wavelet and power spectrum analyses on the two-meter temperature anomalies.\u003c/p\u003e \u003cp\u003eStatistical tests performed in the course of the analysis account for the effects of serial correlation through the use of effective numbers of degrees of freedom \u003csup\u003e\u003cspan additionalcitationids=\"CR46 CR47\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, J.L., B.L. and G.H.; methodology, J.L., and B.L.; data curation, G.H. and J.L.; visualization, J.L.; formal analysis, J.L., and B.L.; writing\u0026mdash;original draft preparation, J.L.; writing\u0026mdash;review and editing, B.L., F.W.Z. and J.J.L.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was supported by the State Key Laboratory of Climate System Prediction and Risk Management (CPRM) initiative project (Grant No. CPRM-2025\u0026not;NUIST-012); Sichuan Science and Technology Program(No.2025YFNH0006); Water Conservancy Science and Technology Project of Jiangsu Province (No. 2025017);the funding of Fengyun Application Pioneering Project (FY- APP); the Bohai Rim Regional Meteorological Science and Technology Collaborative Innovation Fund project(QYXM202409).\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe ERA5 reanalysis dataset is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu\u003c/span\u003e\u003cspan address=\"https://cds.climate.copernicus.eu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003ch2\u003eCode availability\u003c/h2\u003e \u003cp\u003eThe source codes in this study are available to qualified researchers on reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSeneviratne, S., et al., IPCC AR6 WGI Chap. 11: Weather and Climate Extreme Events in a Changing Climate. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCha, E.J., et al., Third assessment on impacts of climate change on tropical cyclones in the Typhoon Committee Region\u0026ndash;Part II: Future projections[J]. 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Wu, Diagnosis and Prediction Method of Climate Variability. 2005, China Meteorological Press: Beijing, China.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"extreme temperature variation, atmospheric circulation, temperature budget, eastern China","lastPublishedDoi":"10.21203/rs.3.rs-9413098/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9413098/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Rapid reversals between warm and cold extremes, sometimes known as extreme temperature variation, are highly-impact climate variability in mid-latitude regions. During the winter of 2023–2024, eastern China experienced its strongest day-to-day surface air temperature variability since 1979. Using ERA5 reanalysis for 1979–2024, we investigate the dynamical and thermodynamic processes underlying this record-breaking event. The results show that intraseasonal variability of the Siberian High (SBH) played a dominant role in this process. Large intraseasonal variability of the SBH reorganized low-level circulation over eastern China and modulated alternating intrusions of cold air from polar and warm air from tropics. The variation of East Asian Trough (EAT) over the Bohai Sea region followed the SBH, as indicated by lead–lag analysis. This consists a vertically coupled pathway reinforced by southeastward-propagating wave train. Thermodynamic diagnosis reveals a marked north–south contrast in the surface temperature anomalies. In North China, temperature variability was governed mainly by diabatic heating linked to SBH–EAT coupling. By contrast, meridional temperature advection and diabatic heating contributed jointly to warm-cold shifting in South China. A long-term comparison further shows that the temperature influence of EAT variability becomes pronounced only under strong SBH variability, validating the SBH as the leading regulator of this winter extreme temperature variation event over eastern China.","manuscriptTitle":"Siberian High variability shaped the record-breaking winter extreme temperature variation over eastern China in 2023–2024","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 09:21:37","doi":"10.21203/rs.3.rs-9413098/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e3a637f8-0501-4b29-b53f-2288c1295dc6","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-12T20:12:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T02:26:42+00:00","index":23,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T01:46:59+00:00","index":22,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T07:41:44+00:00","index":21,"fulltext":""},{"type":"reviewerAgreed","content":"34509028011293355014102134094548765446","date":"2026-04-30T01:20:07+00:00","index":18,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67394385,"name":"Earth and environmental sciences/Climate sciences"},{"id":67394386,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2026-05-12T20:24:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 09:21:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9413098","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9413098","identity":"rs-9413098","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}