Linking the Subseasonal Variability of the East Asia Winter Monsoon and the Madden-Julian Oscillation through Wave Disturbances along the Subtropical Jet

Linking the Subseasonal Variability of the East Asia Winter Monsoon and the Madden-Julian Oscillation through Wave Disturbances along the Subtropical Jet | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Linking the Subseasonal Variability of the East Asia Winter Monsoon and the Madden-Julian Oscillation through Wave Disturbances along the Subtropical Jet Junyi Xiu, Xianan Jiang, Renhe Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5337426/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 May, 2025 Read the published version in npj Climate and Atmospheric Science → Version 1 posted 9 You are reading this latest preprint version Abstract Due to a dramatic increase in the intensity and frequency of global weather extremes in recent decades, there is an urgent demand for reliable subseasonal-to-seasonal (S2S) climate predictions to guide disaster preparedness and policy-making for climate mitigation. However, our latest climate models exhibit rather limited S2S prediction skill, particularly for precipitation, partially due to a lack of understanding of key processes governing regional S2S variability. In this study, we illustrate that the subseasonal variability of precipitation over the East Asian Winter Monsoon (EAWM) region is not only closely tied to activity of the Madden-Julian Oscillation (MJO) over the Indian Ocean, but also linked to precipitation and temperature extremes worldwide, influenced by a circumglobal Rossby wave-train along the subtropical westerly jet. Despite a close phase-lock relationship between the MJO and subseasonal EAWM precipitation, this study confirms a minor role of the MJO itself for the subseasonal EAWM precipitation, which contradicts many previous findings. Considering a crucial role of the circumglobal Rossby wave-train for S2S variability of global weather extremes, we call for significant community efforts towards improved understanding and predictions of the circumglobal Rossby wave-train. The implications of the underexploited predictability by this circumglobal Rossby wave-train for S2S prediction of winter precipitation over the west coast of North America are particularly discussed. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Climate sciences/Atmospheric science Earth and environmental sciences/Climate sciences/Climate change East Asian winter monsoon subseasonal-to-seasonal prediction Madden-Julian Oscillation circumglobal Rossby wave pattern weather extremes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Significance Statement Despite an increase in the intensity and frequency of extreme weather events due to recent human-induced warming, our latest climate models exhibit rather limited skill in predicting these extremes at a lead time from two weeks to one season, i.e., on the subseasonal-to-seasonal (S2S) time-scale, leaving us greatly disadvantaged for disaster prevention and risk management. Focusing on the S2S variability of winter precipitation over East Asia in this study, we illustrate how a circumglobal Rossby wave-train along the subtropical westerly jet exerts widespread influence on weather extremes worldwide. This wave-train can represent an important underexploited predictability source towards improved global S2S climate predictions, such as winter precipitation over the west coast of North America. Introduction Weather and climate extremes, such as heavy precipitation, severe droughts, heat waves, and cold spells, have tremendous environmental and socio-economic impacts. There is mounting evidence that the pattern of occurrence of extreme events, including frequency, intensity, location, and timing, has changed due to the human-induced climate change ( 1 ). Accurate predictions of these extreme events with a lead time from two weeks to one season, i.e., subseasonal-to-seasonal (S2S) predictions, have therefore become an active research area and an urgent need for disaster preparedness, risk management, and guiding policy-making for climate mitigation ( 2 , 3 , 4 ). In general, the strongest S2S variability in precipitation is found in the tropics, often associated with the Madden-Julian Oscillation (MJO; 5, 6). Most recently there have been increasing community efforts in exploring S2S predictability of precipitation in densely populated regions over the extratropics, such as East Asia, the west coast of North America (WNA), and the Mediterranean Sea Region (MSR) ( 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ). For East Asia, while the strongest precipitation usually occurs during the summer monsoon season, extreme precipitation during winter, characterized as an active period of the East Asian Winter Monsoon (EAWM), can also lead to severe meteorological hazards such as long-lasting cold spells, freezing rain, and snowstorms. The S2S variability in winter precipitation over East Asia is generally considered to be associated with southward migration of circulation patterns over the mid-to-high-latitudes of the Eurasian Continent ( 17 , 18 , 19 , 20 ), often accompanied by the cold-air outbreak, and/or modulations by the tropical MJO ( 19 , 21 , 22 , 23 , 24 , 25 , 26 ). While the essential role of MJO convection on the S2S variability of precipitation over East Asia was recently questioned ( 17 ), a critical question remains unaddressed regarding the observed phase-lock relationship between intraseasonal EAWM precipitation and MJO convection as previously reported (e.g., 17, 21, 23, 25, 26). Specifically, the peak intraseasonal precipitation over the EAWM region is often observed when enhanced MJO convection is located over the Eastern Indian Ocean. In this study, we illustrate that fluctuations of EAWM precipitation and MJO activity over the equatorial Indian Ocean tend to be synchronized by eastward-migrating Rossby wave disturbances along the subtropical jet stream, giving rise to their close phase-lock relationship. However, the MJO itself may not significantly contribute to the subseasonal variability of EAWM precipitation. This same Rossby wave-train pattern along the subtropical jet, extending from the west coast of Europe all the way to North America, also tends to link the subseasonal variability of the EAWM to other densely populated extratropical regions, including WNA and MSR. These findings have important implications towards improved S2S prediction of winter extreme events worldwide, for example, over WNA, where a breakthrough in S2S prediction of winter precipitation is urgently needed to guide local water managers coping with recent prolonged droughts ( 9 , 12 ). Results The leading subseasonal variability mode of precipitation associated with the EAWM To extract the leading subseasonal variability mode of precipitation associated with the EAWM, we applied an extended empirical orthogonal function (EEOF) analysis to the 10 − 90-day-filtered anomalous TRMM (Tropical Rainfall Measuring Mission) precipitation ( 27 ) during the extended boreal winter (November-April) from 1998 to 2016 over the East Asia region (18°‒37°N, 90°‒140°E; red box at day 0 in Fig. 1 a), where strong local S2S variability in precipitation has been previously reported (e.g., 17, 19, 23). The leading subseasonal precipitation variability mode, with a prevailing period of about 25 days, is captured by the first pair of EEOF modes that are 90 o out of phase (see Methods for details, also supporting Figs. S1 and S2); its evolution pattern can thus be depicted by lag-regression patterns of the 10–90-day-filtered rainfall anomalies onto the time series of the principal component associated with the first leading EEOF mode (EPC 1 ). As shown in Fig. 1 a, the leading subseasonal mode of precipitation over East Asia is characterized by a north-south dipole pattern of anomalous rain belts extending from Southeastern China to Japan/Korea with a gradual southeastward propagation with time. The peak positive precipitation anomalies over East Asia are observed at day 0, accompanied by southwesterly anomalous low-level winds converging onto the convection region, with an anticyclone to the east and a cyclone to the west of the enhanced precipitation center (Fig. 1 a). The southwesterly low-level winds, on one hand, bring rich moisture from southern China and the western Pacific to sustain vigorous convection; on the other hand, the upward motion induced by the quasi-geostrophic circulation can also play a crucial role in promoting enhanced convection over East Asia (to be elaborated in Fig. 2). Anomalous precipitation and circulation patterns at day − 12 largely mirror those at day 0 with an opposite sign, consistent with a period of about 25 days for the leading subseasonal mode of EAWM precipitation (supporting Fig. S2). Associated with the evolution of EAWM precipitation, the development of enhanced convection is observed over the western equatorial Indian Ocean (IO) near 60 o E at day − 8; it then gradually intensifies and propagates eastward along the equator towards the Maritime Continent after day − 4 (Fig. 1 a), exhibiting typical characteristics of the MJO. In association with strong precipitation over East Asia between day 0 and day 4, the enhanced MJO convection arrives over the eastern equatorial IO, corresponding to an MJO Phase 3 based on the Wheeler-Hendon MJO index (28, WH04). These results are largely in accord with many previous studies that emphasize the important role of the MJO in leading to the subseasonal variability of EAWM precipitation (e.g., 19, 21, 22, 23, 24, 25, 26). Role of the tropical MJO for the subseasonal variability of EAWM precipitation To quantify the contribution of the MJO to the leading subseasonal variability mode of EAWM precipitation, a regression-based approach is applied to derive daily precipitation and circulation anomalies associated with the MJO (see Methods). Evolution patterns of MJO-related precipitation and circulation anomalies associated with the leading subseasonal variability mode of EAWM precipitation can then be obtained by lag-regressions against the EPC 1 time series (Fig. 1 b). The typical MJO evolution features associated with the subseasonal variations of EAWM precipitation are readily seen in Fig. 1 b, including the initiation of an enhanced MJO convection over the western equatorial IO before day − 4, its intensification while slowly migrating eastward along the equator after day − 4, and crossing the Maritime Continent after day 4. The MJO-related precipitation and circulation anomalies, however, are largely confined to the tropics with rather weak signals over the EAWM region, suggesting that the MJO itself may not significantly contribute to the subseasonal variability of EAWM precipitation. Figure 1 c further shows the evolution of the residual precipitation and circulation anomalies after the MJO signals are removed from the total, i.e., non-MJO signals associated with subseasonal precipitation variability over East Asia. The largely identical evolution features in precipitation and circulation anomalies over East Asia between Fig. 1 a and Fig. 1 c confirm the minor role of the MJO in driving the subseasonal variability of EAWM precipitation. This result largely supports the findings in Yao et al. (2015), which suggest that the MJO contributes to about 10% of subseasonal precipitation variability in East Asia. However, it contradicts many previous studies that underscore a crucial role of the MJO for the subseasonal variability of precipitation over the EAWM region. Differences are also noted between this study and Yao et al. (2015). While enhanced subseasonal precipitation over Southern China is found to be largely collocated with low-level cyclonic circulation in Yao et al. (2015; their Fig. 1 a), our analysis suggests that the anomalous positive precipitation center tends to be situated between an anticyclonic circulation to the east and a cyclonic circulation to the west (Fig. 1 , day 0). Moreover, the circumglobal Rossby-wave pattern associated with the subseasonal variability of EAWM precipitation, to be described below, was not evident in Yao et al. (2015), possibly due to an earlier generation of reanalysis dataset used in their study. Triggering of the MJO by circulation patterns associated with EAWM precipitation The close association of enhanced subseasonal precipitation over East Asia with active MJO convection over the IO, but the minor role of the MJO in driving the subseasonal variability of EAWM precipitation, suggests that either the initiation of tropical MJO convection could be triggered by the subseasonal EAWM variability, or both the MJO and the subseasonal variability of EAWM precipitation are regulated by common large-scale factors. Triggering of tropical convection and the MJO by the subseasonal variability of the EAWM, such as cold surges, during its southward propagation has been previously reported (e.g., 17, 29, 30, 31), but mainly over the western Pacific and Maritime Continent regions. In contrast, the initiation of MJO convection as shown in Fig. 1 b occurs over the western equatorial IO before day − 4. In this section, we will closely examine the detailed processes underlying MJO initiation over the western equatorial IO associated with the evolution of the subseasonal variability of EAWM precipitation. Figure 2a illustrates the time-longitude evolution of MJO-related precipitation anomalies along the equator based on lag-regression patterns shown in Fig. 1 b (shaded). The eastward propagation of the MJO associated with the subseasonal EAWM precipitation is clear, particularly over the Indian Ocean sector, with maximum precipitation anomalies located over the eastern IO over 80-100 o E from day 0 to day 5. Consistent with the eastward propagation of convection, MJO-related 850hPa moisture anomalies also exhibit systematic eastward propagation over the IO (Fig. 2a, contours; see Methods for details on extraction of MJO-related anomalous fields), with a phase slightly leading precipitation anomalies by about 2–3 days, signaling the well-known moisture preconditioning process during the MJO development. For example, prior to the initiation of active MJO convection over the western equatorial IO near 60 o E between day − 10 and day − 5, enhanced MJO moisture anomalies start to emerge between day − 15 and day − 10 (Fig. 2a). The evolution of vertical moisture profiles associated with the MJO over the western IO is further illustrated in Fig. 2b (shaded), which is largely consistent with the evolution of anomalous upward motion associated with the MJO (Fig. 2c, shaded). Interestingly, the initiation of positive MJO moisture anomalies over the western IO between day − 15 and day − 10 (Fig. 2b, shaded) tends to closely follow non-MJO moisture anomalies that peak about 10 days earlier (Fig. 2b, contours), which are further closely linked to a maximum of non-MJO anomalous upward motion in the lower-troposphere between 850hPa and 500hPa (Fig. 2c contours), indicating that the initiation of the MJO over the western equatorial IO can be triggered by the non-MJO circulation anomalies. To further illustrate how the non-MJO circulation can initiate the MJO convection over the western equatorial IO, and considering the strongest signals in the non-MJO moisture (Fig. 2b) and vertical velocity (Fig. 2c) anomalies confined to the lower-troposphere, Fig. 2d presents anomalous non-MJO wind (vectors), moisture (shaded), and vertical velocity derived based on the quasi-geostrophic equation (QG \(\:\omega\:\) ; 32, 33; contours) averaged between 850-500hPa and between day − 15 and day − 10. The non-MJO low-level moistening during this period, which can be critical for the MJO initiation as shown in Fig. 2b, is mainly evident over the Arabian Sea between the equator and 20 o N, in the southeastern portion of a cyclonic circulation (Mark “A”) as a part of a wave-train linking to the EAWM. Enhanced moisture over this region can be ascribed to the QG upward motion over the eastern part of the cyclonic circulation (Fig. 2d, contours), which is largely induced by the warm temperature advection, and by the horizontal moisture advection due to southerly anomalous winds (figures not shown). In contrast, over the northwestern part of the cyclone, i.e., over northwestern India, the Mid-East, and East Africa, the prevailing non-MJO anomalous downward QG motion and northerly winds lead to drying of the lower troposphere. A very similar cyclonic circulation pattern is also evident over East Asia at this time (“Mark B”, Fig. 2d), the dryness over the EAWM region associated with the downward QG vertical motion on the west of the cyclone is also consistent with a suppressed phase of the EAWM precipitation (Fig. 1 a, day − 12), in contrast to a wet condition and thus enhanced precipitation over the western North Pacific (WNP) to the east of the Philippines, in association with QG upward motion in the southeast of the cyclonic circulation. These results clearly suggest that the initiation of the MJO convection over the western equatorial IO and the subseasonal variability of EAWM precipitation are intimately linked to each other through the zonally elongated wave-train pattern. While the extratropical origin of the MJO has been suggested by previous studies based on either case studies ( 34 , 35 ) or model experiments ( 36 , 37 , 38 ), results in this study clearly demonstrate the triggering of tropical MJO convection by a subtropical Rossby wave-train when passing over the northern Indian Ocean. While results in Fig. 2b suggest that the growth of the MJO moisture perturbations over the western IO tends to follow a peak phase of the non-MJO moisture and vertical velocity anomalies, the detailed processes underlying the phase lag between the MJO and non-MJO moisture anomalies need to be further understood. In addition to the thermodynamic processes, there is also the possibility that the extratropical Rossby wave disturbances centered over the Arabian Sea, as shown in Fig. 2d, could directly induce tropical Kelvin wave responses and thus trigger the initiation of MJO convection over the equatorial western IO, for example, through a “Sverdrup effect” as proposed by a recent idealized modeling study ( 39 ). The global Rossby-wave pattern linking the EAWM with weather extremes worldwide Considering the crucial role of the subtropical Rossby wave pattern in linking tropical MJO activity over the IO and the subseasonal variability of EAWM precipitation, the detailed characteristics of this subtropical Rossby wave-train are further examined in this section, particularly from a large-scale perspective. Figure 3 illustrates the distribution of global 500hPa wind along with surface precipitation and temperature anomalies associated with the evolution of subseasonal EAWM precipitation. It is evident that the subtropical Rossby wave pattern over the Asian sector, which links the MJO initiation over the IO and the EAWM precipitation as previously discussed, is part of a global Rossby wave pattern with a wavenumber-6. This Rossby wave pattern extends from the North Atlantic, via the MSR, India, East Asia, the North Pacific, all the way to North America and the Gulf of Mexico, largely following a wave-guide along the subtropical westerly jet (Fig. 4 ). It exhibits an eastward propagation with a phase speed of about 3 degrees per day over the Indo-Pacific sector (figure not shown). Given a wavelength of about 70 degrees of the Rossby wave train over the Asian sector, as roughly estimated by the distance between the two centers of cyclonic circulations (denoted by “A” and “B” in Fig. 2d), this propagation speed is largely in accord with the observed subseasonal period of about 25 days associated with EAWM precipitation variability. While a close association between the Rossby wave pattern and precipitation over the EAWM region and the tropical Indo-Pacific region, as previously discussed, is again illustrated in Fig. 3 (shaded), widespread impacts of this Rossby wave pattern on precipitation anomalies beyond the Indo-Pacific sector are readily discerned. These include the west coast of the Iberian Peninsula, MSR, the North Pacific, Alaska, WNA, the Gulf of Mexico, and even equatorial west Africa and the Amazon Basin in South America. For example, in association with the peak phase of the subseasonal EAWM precipitation at day 0, reduced precipitation anomalies are found near the MSR, over the north Pacific and Alaska, and the Gulf of Mexico, while enhanced precipitation is observed over the west coast of the Iberian Peninsula, WNA, and the Amazon Basin. In addition to precipitation, this circumglobal Rossby wave pattern also exerts significant influences on surface temperature (color dots in Fig. 3 ). For example, from day − 12 to day 0, associated with the transition of EAWM precipitation from a negative to a positive phase, a patch of cold surface temperature anomalies gradually intensifies and propagates southeastward from the MSR to the Mid-East/North Africa. Meanwhile, at day − 12 and day − 8, along with suppressed precipitation over East Asia, a so-called “Warm Arctic-Cold Continent (WACC)” pattern in surface temperature anomalies is discerned over North America. This WACC pattern has been reported as a leading subseasonal variability mode of wintertime surface temperature over North America and exerts significant modulations on regional temperature extremes and Arctic sea ice ( 40 , 41 , 42 ). To illustrate the energy sources sustaining this subseasonal circumglobal Rossby wave pattern, Fig. 4 a illustrates the evolution of geopotential height ( \(\:{\phi\:}\) ) anomalies and wave activity fluxes (WAF; 43; see Methods) at 500hPa associated with the subseasonal EAWM precipitation. It is clear that the alternating positive and negative \(\:{\phi\:}\) anomalies along the wave train are largely aligned along the subtropical jet stream wave-guide (contours in Fig. 4 a). Due to the presence of the Tibetan Plateau, the southward shift of the subtropical jet over the Indian sector creates a unique environment to interact with the tropics and initiate tropical MJO convection as discussed earlier. At day − 12, while the strongest \(\:{\phi\:}\) anomalies and WAF are found over the North Pacific/North American sector, these anomalies are significantly weakened afterwards. At day − 8, wave disturbances over the Eurasian sector start to intensify, associated with the establishment of an anomalous high over the Iberian Peninsula and injection of strong WAF into the entrance region of the subtropical jet near North Africa and the MSR region. A strong divergence in WAF is seen over the west coast of the Iberian Peninsula at day − 8 (Fig. 4 a), with rather weak wave fluxes from upstream, suggesting that the intensification of the Rossby wave-train downstream could be closely associated with the establishment of the anomalous high over the Iberian Peninsula. It is also noteworthy that the presence of positive \(\:\phi\:\) anomalies over both the Iberian Peninsula and the west North Atlantic near 60 o W, and negative \(\:\phi\:\) anomalies over Iceland at day − 8, is strongly reminiscent of a positive phase of the North Atlantic Oscillation (NAO), indicating a potential role of the NAO in generating the circumglobal Rossby wave train along the subtropical jet stream. After day − 8, the eastward propagation of the Rossby-wave energy downstream along the subtropical jet is clearly discerned. At day 0, strong wave disturbances with a reversed sign to those at day − 12 re-emerge over the North Pacific/North America (Fig. 4 a). Evolution of the Rossby wave pattern and associated WAF is further illustrated in Fig. 4 b by showing the vertical-longitude profiles of \(\:{\phi\:}\) anomalies and WAF averaged over the latitude belt between 25 o N and 40 o N, where the subtropical jet stream is largely situated over the Eurasian sector. The downstream propagation of Rossby-wave energy with a maximum around 250hPa, and a gradual eastward amplification of wave disturbances are again clearly seen. Particularly noteworthy that the rather weak WAF in the central North Pacific at day − 8 again suggests that processes associated with the establishment of the anomalous high over the Iberian Peninsula, a southern lobe of a positive NAO pattern, may play a critical role in energizing the Rossby wave train downstream along the subtropical jet. Note that a zonally-extended Rossby wave pattern along the subtropical jet over the Eurasian sector during boreal winter has been widely reported in the variability of monthly or seasonal mean circulation anomalies ( 44 , 45 , 46 , 47 , 48 , 49 ), or on the synoptic time-scale associated with extreme precipitation and temperature events over East Asia ( 50 , 51 , 52 , 53 , 54 ). However, rather limited studies have focused on the subseasonal variability of the Rossby wave train along the subtropical Asian jet, particularly on its influence on global precipitation during the boreal winter season. While the subseasonal wave pattern along the subtropical jet can generate the subseasonal variability of EAWM precipitation as shown in this study, it is possible that the EAWM precipitation variability can also play a crucial role in triggering and maintaining these extratropical Rossby wave patterns as suggested by previous studies ( 55 ). Discussion Despite the urgent need for accurate S2S predictions to guide disaster preparedness and climate mitigation policy-making, our current S2S prediction skill remains rather limited, particularly for precipitation, partially due to a lack of understanding of key processes governing regional S2S variability. Focusing on the S2S variability of winter precipitation over East Asia in this study, we illustrate that subseasonal fluctuations of EAWM precipitation tend not only to be synchronized to MJO activity over the Indian Ocean, but also to be intimately linked to subseasonal variations of precipitation and surface temperature worldwide, through modulations by eastward-migrating Rossby wave-trains along the subtropical westerly jet. As further shown by Fig. 5 , beyond the tropical region, which features vigorous tropical convective variability, the strongest subseasonal variability of precipitation over the extratropics is largely observed along the latitudinal belt collocated with the circumglobal Rossby wave pattern. This is particularly evident over regions subject to further modulations by local topography along coastal areas, such as the west coast of the Iberian Peninsula, the coastal regions around MSR, the Bay of Bengal, Southeast China, and WNA. Particularly, the southward displacement of the subtropical westerly jet over the Indian sector due to topographic impacts from the Tibetan Plateau creates a unique environment for this subtropical Rossby wave-train to interact with tropical convection. For instance, during the suppressed phase of subseasonal precipitation over the EAWM region, a lower-tropospheric trough is found over the Arabian Sea around 20 o N as part of the Rossby wave-train (Fig. 2d, Fig. 3 a). Over the southeastern portion of the anomalous cyclonic circulation between the equator and 15 o N, enhanced moisture anomalies are discerned, associated with the quasi-geostrophic anomalous ascending motion due to warm temperature advection and horizontal moisture advection associated with the southwesterly anomalous winds (Fig. 2d). Subsequently, MJO convection starts to initiate over the western Indian Ocean, then intensifies and propagates eastward along the equator. After about 10–12 days, the previous cyclonic Rossby wave pattern over the Arabian Sea moves eastward over East Asia, promoting enhanced precipitation of the EAWM. Meanwhile, the MJO convection arrives over the eastern Indian Ocean, featuring a phase-lock between the enhanced subseasonal precipitation over East Asia and active MJO convection over the eastern Indian Ocean as previously reported. A decomposition of the total subseasonal EAWM precipitation into an MJO-related component and a non-MJO component, however, suggests that the MJO itself does not play a crucial role in the subseasonal EAWM precipitation. This contradicts many other studies that propose a crucial role of the MJO in the subseasonal variability of EAWM precipitation largely based on the aforementioned phase-lock relationship. In light of the profound influences of the circumglobal Rossby wave-train on the subseasonal variability of precipitation and temperature as illustrated in this study, these wave patterns can serve as critical predictability sources for S2S predictions over extensive regions along the subtropical westerly jet. For example, despite the severe long-lasting droughts over WNA in recent years, our state-of-the-art climate models exhibit rather limited skill for S2S prediction of winter precipitation over this region ( 12 , 56 , 57 ). The El Niño-Southern Oscillation (ENSO) has been used as a primary factor for seasonal prediction of winter precipitation over WNA, with wet (dry) conditions during El Niño (La Niña) winters ( 58 , 59 ). However, the unexpected extremely wet condition occurred over California during the 2022/2023 winter, was associated with a La Niña condition over the eastern Pacific, and was unpredictable by our major prediction systems ( 57 ). Recent analyses indicate that the extremely wet condition over WNA during the 2022/2023 winter was closely associated with eastward-propagating Rossby wave disturbances from East Asia along the subtropical westerly jet ( 60 ), as discussed in this study. The linkage of the cross-Pacific short Rossby wave-trains, which are independent from the El Niño, to the S2S variability of precipitation over WNA has also been reported in several recent studies (e.g., 12, 61, 62). This lends further evidence of the importance of the circumglobal Rossby wave-trains for S2S predictions of global weather and climate extremes. Results in this study suggest that the establishment of the circumglobal Rossby wave train associated with the subseasonal variability of EAWM precipitation is closely associated with the formation of an anomalous high near the Iberian Peninsula, resembling a southern branch of the circulation anomalies during a positive phase of the NAO (Fig. 4 , day − 8). Considering the MJO initiation over the Indian Ocean associated with the Rossby wave-train, this indicates a possible pathway of the NAO influences on the MJO over the Indian Ocean through the circumglobal subtropical Rossby waves. However, the positive NAO-like pattern about 8 days before the peak MJO convection over the eastern IO (i.e., Wheeler-Hendon MJO Phase 3) as suggested in this study is in contrast to the findings in previous studies (e.g., 63, 64), which reported that enhanced MJO convection over the Indian Ocean often follows a negative phase of the NAO pattern over the Atlantic sector. Further investigations are needed to fully understand these discrepancies. One possibility is that results shown in this study are associated with the subseasonal variability of EAWM precipitation; therefore, the NAO-like patterns and the MJO events captured in this study could only represent a small subset of total NAO and MJO events. For example, previous modeling studies suggested that the NAO condition is not necessary for exciting the circumglobal Rossby waves (e.g., 65, 66). It will be interesting to examine the percentage of MJO events over the IO initiated by the circumglobal subtropical Rossby waves, and the percentage of the circumglobal Rossby waves associated with the NAO. While the subtropical circumglobal Rossby wave-train associated with subseasonal EAWM precipitation shown in this study largely exhibits similar characteristics to those previously reported associated with the monthly or seasonal mean anomalies, differences in the Rossby wave patterns on different time scales are also evident. For example, while a global wavenumber-5 structure of the circumglobal Rossby wave train has been reported based on monthly or seasonal anomalous fields during boreal winter (e.g., 45), a wavenumber-6 pattern is found in the subseasonal Rossby-wave train in this study (Fig. 3 , also Fig. 5 ). As previously discussed, it is the unique wavelength (~ 70 o ) and propagation phase speed (3 degrees per day) of the subseasonal Rossby wave pattern over the Indo-Pacific sector that determines a prevailing time-scale of about 25 days in winter precipitation over East Asia. Therefore, the large-scale factors determining the formation, amplitude, and phase of these subtropical circumglobal Rossby wave patterns on the subseasonal time scale, could also be different from those on monthly and seasonal time scales, which also warrants further investigations. Materials and Methods Observational datasets Daily variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 reanalysis ( 67 ) are used to characterize large-scale patterns associated with the subseasonal variability of precipitation over East Asia. These valuables include surface air temperature, 3D geopotential height ( \(\:{\phi\:}\) ), meridional and zonal winds (u, v), vertical velocity (w), and specific humidity (q) on 0.75°×0.75° horizontal grids and 37 vertical pressure levels from 1000 to 1hPa. Daily precipitation data from the Tropical Rainfall Measuring Mission (TRMM, version 3B42; 68) is used to extract the leading mode of the subseasonal variability of precipitation associated with the EAWM and the Madden-Julian Oscillation. Precipitation from the Global Precipitation Climatology Project (GPCP; 69) is used to characterize global precipitation anomalies associated with the leading subseasonal variability mode of EAWM considering its global coverage. Analyses in this study mainly focus on the extended boreal winter from November to March during the period of 1998–2016. To extract the subseasonal variability signals, a 10 − 90-day band-pass filtering ( 70 ) is applied to daily anomalies of each variable after removal of the climatological annual cycle (annual mean plus three leading harmonics). The leading subseasonal variability mode of the East Asia winter monsoon Instead of an empirical orthogonal function (EOF) analysis used in many previous studies (e.g., 17), we employ an extended EOF (EEOF) analysis ( 71 , 72 ) of 10–90 day filtered daily TRMM precipitation anomalies during the 1998–2016 winters (November-March) to identify the dominant subseasonal variability mode of EAWM precipitation. The EEOF is essentially the same as the EOF just using an extended covariance matrix with the daily data during all these time lags. The EEOF analysis is conducted over the region of 17.5°-35°N and 95°E-140°E (see the red box in Fig. 1 a at day 0) with a time lag of 31 days. The derived eigenvectors based on the EEOF analysis therefore contain a series of evolution patterns of the leading modes. As shown in Figs. S1 and S2, the first and second modes (EEOF 1 and EEOF 2 ), with a same prevailing period of 25 days (0.04 cycle/day), explain about 4.9% and 4.7% of the total subseasonal precipitation variances, respectively, and thus represent the same leading subseasonal mode of the EAWM precipitation with a 90-degree phase difference. In this study, various large-scale variables associated with the evolution of the leading subseasonal precipitation mode over East Asia are derived by lag regressions of their corresponding 10-90-day filtered anomalies against the principal component of the EEOF 1 (EPC 1 ). Identification of the MJO and its associated signals To examine the role of the MJO in contributing to the subseasonal variability of EAWM precipitation, we decompose the total anomalous precipitation pattern associated with the subseasonal EAWM precipitation variability (Fig. 1 a) into two components: precipitation anomalies associated with the MJO (Fig. 1 b) and dependent from the MJO (the non-MJO component; Fig. 1 c), with the non-MJO precipitation anomalies derived by subtracting the MJO component from the total precipitation anomalies. Since we focus on the remote influences of MJO convection on the EAWM, and considering the caveat of the Wheeler-Hendon MJO index due to blended low-frequency variability signals ( 73 ), we employ an EOF analysis of daily precipitation anomalies for 1998–2016 winters (November-March) to identify the dominant MJO convective signals. The EOF analysis of 10-90-day filtered TRMM precipitation anomalies is conducted over the equatorial Indo-Pacific region, 15°S–15°N, 40°–160°E, where MJO convection is most active. The two leading EOF modes capture typical anomalous rainfall patterns of the MJO at their different phases during its eastward propagation, i.e., with enhanced MJO convection over the eastern equatorial Indian Ocean in EOF 1 and the Maritime Continent in EOF 2 (Fig. S3). The two leading principal components (PC 1 and PC 2 ) are then used to derive typical patterns of any 2D or 3D variable ( \(\:\text{Z}\) ) associated with the two leading EOF modes of the MJO by regressing the daily 10-90-day filtered anomalies of the variable ( \(\:{\text{Z}}^{{\prime\:}}\) ) on to PC 1 and PC 2 , and obtain their regressed patterns of RegZ 1 and RegZ 2 corresponding to the 1st and 2nd leading modes of the MJO, respectively. Anomalies of this variable associated with the MJO ( \(\:{\text{Z}}_{mjo}^{{\prime\:}}\) ) on a particular day for 1998–2016 winters can then be derived by the regression patterns normalized by the amplitude of PC 1 and PC 2 on that day, i.e., \(\:{\text{Z}}_{mjo}^{{\prime\:}}\) = RegZ 1 *PC 1 + RegZ 1 *PC 2 Meanwhile, anomalous fields that are independent of the MJO (i.e., the non-MJO component, \(\:{\text{Z}}_{non\_mjo}^{{\prime\:}}\) ) can be obtained by subtracting the MJO-related components ( \(\:{\text{Z}}_{mjo}^{{\prime\:}}\) ) from the total 10-90-day filtered anomalies ( \(\:{\text{Z}}^{{\prime\:}}\) ), i.e., \(\:{\text{Z}}_{non\_mjo}^{{\prime\:}}\) = \(\:{\text{Z}}^{{\prime\:}}-{\text{Z}}_{mjo}^{{\prime\:}}\) . Note that a similar regression approach was used in Yao et al. (2015) to derive precipitation and circulation patterns associated with the MJO, but based on the WH04 MJO index. The evolution patterns of the variable \(\:\text{Z}\) , as well as its MJO and non-MJO components, associated with the leading subseasonal variability mode of EAWM precipitation can be further derived by lag-regressions of \(\:{\text{Z}}^{{\prime\:}}\) , \(\:{\text{Z}}_{mjo}^{{\prime\:}}\) , \(\:{\text{Z}}_{non\_mjo}^{{\prime\:}}\:\) against the time series of EPC 1 , respectively, as shown in Fig. 1 for the precipitation and 800hPa winds. Wave activity flux (WAF) An analysis of the 3D wave activity flux (WAF) is conducted to explore possible energy sources in sustaining the subtropical Rossby wave rain associated with the subseasonal variability of EAWM precipitation. The calculation of 3D WAF is based on the W-vector following Takaya and Nakamura ( 43 ), which is expressed as $$\:\mathbf{W}=\frac{\text{p}}{2\left|\mathbf{U}\right|}\left\{\begin{array}{c}U\left({{{\psi\:}}_{\text{x}}^{{\prime\:}2}{-{\psi\:}}^{{\prime\:}}{\psi\:}}_{\text{x}\text{x}}^{{\prime\:}}\right)+V\left({{{{\psi\:}}_{\text{x}}^{{\prime\:}}{\psi\:}}_{\text{y}}^{{\prime\:}}{-{\psi\:}}^{{\prime\:}}{\psi\:}}_{\text{x}\text{y}}^{{\prime\:}}\right)\\\:U\left({{{{\psi\:}}_{\text{x}}^{{\prime\:}}{\psi\:}}_{\text{y}}^{{\prime\:}}{-{\psi\:}}^{{\prime\:}}{\psi\:}}_{\text{x}\text{y}}^{{\prime\:}}\right)+V\left({{{\psi\:}}_{\text{y}}^{{\prime\:}2}{-{\psi\:}}^{{\prime\:}}{\psi\:}}_{\text{y}\text{y}}^{{\prime\:}}\right)\\\:\frac{{\text{f}}_{0}^{2}}{{\text{N}}^{2}}\left[\text{U}\left({{{{\psi\:}}_{\text{x}}^{{\prime\:}}{\psi\:}}_{\text{z}}^{{\prime\:}}{-{\psi\:}}^{{\prime\:}}{\psi\:}}_{\text{x}\text{z}}^{{\prime\:}}\right)+\text{V}\left({{{{\psi\:}}_{\text{y}}^{{\prime\:}}{\psi\:}}_{\text{z}}^{{\prime\:}}{-{\psi\:}}^{{\prime\:}}{\psi\:}}_{\text{y}\text{z}}^{{\prime\:}}\right)\right]\end{array}\right\}$$ where \(\:{{\psi\:}}^{{\prime\:}}\) is the perturbation stream-function ( \(\:{\psi\:})\) , derived based on lag-regressions of 10–90 day filtered \(\:{{\psi\:}}^{{\prime\:}}\) against the EPC 1 of the leading EAWM precipitation mode; U and V are winter mean zonal and meridional winds, respectively; \(\:{\text{f}}_{0}\) is the Coriolis parameter; \(\:{\text{N}}^{2}\:\) is the buoyancy frequency; p is the normalized pressure by 1000 hPa; and the subscripts represent partial derivatives in the corresponding x, y and z directions. Declarations Data availability The ERA5 data was downloaded from the website https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels. The daily GPCP precipitation data can be assessed at https://www.ncei.noaa.gov/data/global-precipitation-climatology-project-gpcp-daily/access/. The TRMM 3B42 rainfall data was downloaded from https://disc.gsfc.nasa.gov/datasets/TRMM_3B42_7/summary (DOI: 10.5067/TRMM/TMPA/3H/7). Acknowledgements This study is supported by the National Natural Science Foundation of China (42288101). Author contributions J.-Y. X., X. J., and R. Z. designed research; J.-Y. X. carried out the analyses; and J.-Y. X., X. J., and R.Z. wrote the paper. Additional Information Competing interests : The authors declare no competing interest. 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Supplementary Files SupportingMaterialsfor.docx Cite Share Download PDF Status: Published Journal Publication published 16 May, 2025 Read the published version in npj Climate and Atmospheric Science → Version 1 posted Editorial decision: Revision requested 04 Feb, 2025 Reviews received at journal 27 Jan, 2025 Reviews received at journal 12 Dec, 2024 Reviewers agreed at journal 21 Nov, 2024 Reviewers agreed at journal 18 Nov, 2024 Reviewers invited by journal 18 Nov, 2024 Editor assigned by journal 01 Nov, 2024 Submission checks completed at journal 31 Oct, 2024 First submitted to journal 26 Oct, 2024 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. 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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-5337426","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":373874282,"identity":"52dd368f-3f4d-4cf8-b634-063fe8df617d","order_by":0,"name":"Junyi Xiu","email":"","orcid":"","institution":"CMA Earth System Modeling and Prediction Center, China Meteorological Administration","correspondingAuthor":false,"prefix":"","firstName":"Junyi","middleName":"","lastName":"Xiu","suffix":""},{"id":373874283,"identity":"f2a93d23-a98d-47e7-83d5-ba2e5a29d944","order_by":1,"name":"Xianan Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYDACZjBpw8DADqLZiNeSBmUQpQUCDpOgxeA487MHP3ecT+xvZn7A8KHsMGEtks1s5oa9Z24nzjjMZsA44xwRWviZGcwkeNtuJ25gZjBg5m0jQgsbM/s3yb9t54Ba2D8w/yVGCz8zj5k0b9sBoBYeA2ZGYrRINvOUScu2JRvPOMxTcLDnXDphLQbnj2+TfNtmJ9vf3r7xwY8ya8JaUMABEtWPglEwCkbBKMAFACz1M/cLrDCrAAAAAElFTkSuQmCC","orcid":"","institution":"Joint Institute for Regional Earth System Science and Engineering, University of California","correspondingAuthor":true,"prefix":"","firstName":"Xianan","middleName":"","lastName":"Jiang","suffix":""},{"id":373874284,"identity":"72dc5fa6-4dcf-4745-9739-953b285bc9e1","order_by":2,"name":"Renhe Zhang","email":"","orcid":"","institution":"Department of Atmospheric and Oceanic Sciences / Institute of Atmospheric Sciences, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Renhe","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-10-26 12:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5337426/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5337426/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41612-025-01076-y","type":"published","date":"2025-05-16T15:58:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68811661,"identity":"db219260-2c7b-46f7-bc60-fd00e1c16481","added_by":"auto","created_at":"2024-11-12 09:16:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":283159,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of anomalous precipitation (shaded with the color bar on the right; Units: mm/day; based on TRMM) and 850hPa winds (vectors with the scale on the upper-right; speed less than 0.05 m s\u003csup\u003e-1\u003c/sup\u003e are omitted) associated with the leading subseasonal precipitation variability mode over East Asia during boreal winter (left column). These evolution patterns are derived by lead-lag regressions of 10-90-day filtered precipitation and wind anomalies against the EPC\u003csub\u003e1\u003c/sub\u003e corresponding to the EEOF\u003csub\u003e1\u003c/sub\u003e mode of 10-90-day filtered winter precipitation over East Asia (the red box at day 0). The total anomalies on the left column are separated into MJO-related anomalies (middle column), and non-MJO anomalies (right column). Areas with dark grey dots indicate precipitation anomalies surpassing the 95% statistical significance level based on their corresponding correlation coefficients using the Student’s t-test. See Methods for details on identifying the leading subseasonal EAWM precipitation mode and the separation of the MJO-related and non-MJO components.\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-5337426/v1/1090e12a1b2527455e00c86e.png"},{"id":68813270,"identity":"1be0c572-bf7b-4e0c-badf-3d4fe8ac6c8c","added_by":"auto","created_at":"2024-11-12 09:24:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":187067,"visible":true,"origin":"","legend":"\u003cp\u003ea) Longitude–time evolution of MJO-related precipitation anomalies (shaded; units: mm day\u003csup\u003e-1\u003c/sup\u003e; based on TRMM) and 850hPa MJO-related specific humidity anomalies (q; contours with intervals of 10\u003csup\u003e−4\u003c/sup\u003e\u0026nbsp;g kg\u003csup\u003e−1\u003c/sup\u003e, dashed if negative with zero contours omitted). Both variables are averaged over 15\u003csup\u003eo\u003c/sup\u003eS-15\u003csup\u003eo\u003c/sup\u003eN; b) Pressure–time cross-sections of MJO-related (shaded with units of 10\u003csup\u003e−4\u003c/sup\u003e\u0026nbsp;g kg\u003csup\u003e−1\u003c/sup\u003e) and non-MJO (contours with intervals of 10\u003csup\u003e−4\u003c/sup\u003e\u0026nbsp;g kg\u003csup\u003e−1\u003c/sup\u003e, dashed if negative with zero contours omitted) specific humidity anomalies over the western equatorial Indian Ocean (55-65\u003csup\u003eo\u003c/sup\u003eE; 15\u003csup\u003eo\u003c/sup\u003eS-15\u003csup\u003eo\u003c/sup\u003eN); c) similar to b) but shaded for MJO-related\u0026nbsp; and contours for non-MJO vertical velocity anomalies (units: Pa s\u003csup\u003e-1\u003c/sup\u003e; intervals of 10\u003csup\u003e−4\u003c/sup\u003e\u0026nbsp;Pa s\u003csup\u003e-1\u003c/sup\u003e); d) 850-500hPa vertically averaged non-MJO components of anomalous specific humidity (shaded; unit: g kg\u003csup\u003e-1\u003c/sup\u003e), winds (vectors; see the scale on the upper-right of the panel; vectors with wind speed less than 0.05 ms\u003csup\u003e-1\u003c/sup\u003e omitted), and vertical p-velocity derived based on the quasi-geostrophic omega-equation (contours with intervals of Pa s\u003csup\u003e-1\u003c/sup\u003e, dashed if negative with zero contours omitted). The labels “A” and “B” denote centers of the two cyclones over the Arabian Sea and East Asia, respectively, along the circumglobal Rossby wave pattern.\u003c/p\u003e","description":"","filename":"Picture2.png","url":"https://assets-eu.researchsquare.com/files/rs-5337426/v1/e163849f90de3330b366059e.png"},{"id":68811666,"identity":"ef9a684f-ff6d-4862-8df1-aba5fb3fe90f","added_by":"auto","created_at":"2024-11-12 09:16:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":249867,"visible":true,"origin":"","legend":"\u003cp\u003eSimilar as for the left column in Fig. 1 but for total precipitation anomalies (shaded with units of mm day\u003csup\u003e-1\u003c/sup\u003e based on GPCP), wind anomalies at 500hPa (vectors with speed less than 0.08 m s\u003csup\u003e-1\u003c/sup\u003e omitted), and surface temperature anomalies (blue and red dots for positive and negative values; see the legend on the right). For precipitation and surface temperature, only anomalies surpassing the 95% statistical significance level are plotted, which is based on their corresponding correlation coefficients using the student t-test.\u003c/p\u003e","description":"","filename":"Picture3.png","url":"https://assets-eu.researchsquare.com/files/rs-5337426/v1/582d8977cd7b98d9e8cd89dd.png"},{"id":68811663,"identity":"8e841627-e471-4f08-a608-e6b01d2f9405","added_by":"auto","created_at":"2024-11-12 09:16:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":83706,"visible":true,"origin":"","legend":"\u003cp\u003ea) Similarly as in Fig. 1a based on lead-lag regressions against EPC\u003csub\u003e1\u003c/sub\u003e of the leading subseasonal EAWM precipitation mode, but for evolution of geopotential height anomalies (shaded; color bar on the right with units of gpm) and associated horizontal wave activity fluxes at 500hPa (vectors with the scale on the upper-right; amplitude less than 0.2 m\u003csup\u003e2\u003c/sup\u003e s\u003csup\u003e−2\u003c/sup\u003e omitted); b) Evolution of pressure-longitude cross-sections of geopotential height anomalies (shaded) along with the vertical and zonal components of wave activity fluxes (vectors, see the scale on the upper-right with units of m\u003csup\u003e2\u003c/sup\u003e s\u003csup\u003e−2\u003c/sup\u003e for the horizontal and 10\u003csup\u003e−3\u003c/sup\u003e m\u003csup\u003e2\u003c/sup\u003e s\u003csup\u003e−2\u003c/sup\u003e for the vertical components, respectively). Variables in b) are averaged over 25°–40°N where the subtropical westerly jet is located over the Eurasian sector.\u003c/p\u003e","description":"","filename":"Picture4.png","url":"https://assets-eu.researchsquare.com/files/rs-5337426/v1/183623de78f2de9c999eade2.png"},{"id":68811662,"identity":"77609257-db9a-44ca-af47-d5e4cc6a6ac3","added_by":"auto","created_at":"2024-11-12 09:16:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":98378,"visible":true,"origin":"","legend":"\u003cp\u003eA schematic to demonstrate the crucial role of the circumglobal Rossby wave pattern associated with the leading subseasonal variability mode of EAWM precipitation and the subseasonal-to-seasonal precipitation variability over global extratropics. Contours: the wavenumber-6 circumglobal Rossby wave pattern as represented by 300hPa meridional v-wind anomalies based on regression onto the EPC1 at day 0. Shaded: standard deviations of 10-30-day filtered daily precipitation during boreal winter (see the color bar). The selection of a band-pass filtering of 10-30day here is based on the consideration of the prevailing period of about 25 days of the leading subseasonal precipitation variability mode over East Asia.\u003c/p\u003e","description":"","filename":"Picture5.png","url":"https://assets-eu.researchsquare.com/files/rs-5337426/v1/acb515bd56965656e0efecb0.png"},{"id":83068569,"identity":"0412af14-d4b4-4306-a725-4eef3f4387c4","added_by":"auto","created_at":"2025-05-19 16:10:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1768054,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5337426/v1/08615ff7-7f2a-4e97-b8f2-3a9c024094cb.pdf"},{"id":68813271,"identity":"5d679f1b-46f2-4b8b-bcc5-0556338d0b8a","added_by":"auto","created_at":"2024-11-12 09:24:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2088379,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingMaterialsfor.docx","url":"https://assets-eu.researchsquare.com/files/rs-5337426/v1/81209fe8b2a07d0feffe648a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Linking the Subseasonal Variability of the East Asia Winter Monsoon and the Madden-Julian Oscillation through Wave Disturbances along the Subtropical Jet","fulltext":[{"header":"Significance Statement","content":"\u003cp\u003eDespite an increase in the intensity and frequency of extreme weather events due to recent human-induced warming, our latest climate models exhibit rather limited skill in predicting these extremes at a lead time from two weeks to one season, i.e., on the subseasonal-to-seasonal (S2S) time-scale, leaving us greatly disadvantaged for disaster prevention and risk management. Focusing on the S2S variability of winter precipitation over East Asia in this study, we illustrate how a circumglobal Rossby wave-train along the subtropical westerly jet exerts widespread influence on weather extremes worldwide. This wave-train can represent an important underexploited predictability source towards improved global S2S climate predictions, such as winter precipitation over the west coast of North America.\u0026nbsp;\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eWeather and climate extremes, such as heavy precipitation, severe droughts, heat waves, and cold spells, have tremendous environmental and socio-economic impacts. There is mounting evidence that the pattern of occurrence of extreme events, including frequency, intensity, location, and timing, has changed due to the human-induced climate change (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Accurate predictions of these extreme events with a lead time from two weeks to one season, i.e., subseasonal-to-seasonal (S2S) predictions, have therefore become an active research area and an urgent need for disaster preparedness, risk management, and guiding policy-making for climate mitigation (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn general, the strongest S2S variability in precipitation is found in the tropics, often associated with the Madden-Julian Oscillation (MJO; 5, 6). Most recently there have been increasing community efforts in exploring S2S predictability of precipitation in densely populated regions over the extratropics, such as East Asia, the west coast of North America (WNA), and the Mediterranean Sea Region (MSR) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). For East Asia, while the strongest precipitation usually occurs during the summer monsoon season, extreme precipitation during winter, characterized as an active period of the East Asian Winter Monsoon (EAWM), can also lead to severe meteorological hazards such as long-lasting cold spells, freezing rain, and snowstorms.\u003c/p\u003e \u003cp\u003eThe S2S variability in winter precipitation over East Asia is generally considered to be associated with southward migration of circulation patterns over the mid-to-high-latitudes of the Eurasian Continent (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), often accompanied by the cold-air outbreak, and/or modulations by the tropical MJO (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). While the essential role of MJO convection on the S2S variability of precipitation over East Asia was recently questioned (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), a critical question remains unaddressed regarding the observed phase-lock relationship between intraseasonal EAWM precipitation and MJO convection as previously reported (e.g., 17, 21, 23, 25, 26). Specifically, the peak intraseasonal precipitation over the EAWM region is often observed when enhanced MJO convection is located over the Eastern Indian Ocean.\u003c/p\u003e \u003cp\u003eIn this study, we illustrate that fluctuations of EAWM precipitation and MJO activity over the equatorial Indian Ocean tend to be synchronized by eastward-migrating Rossby wave disturbances along the subtropical jet stream, giving rise to their close phase-lock relationship. However, the MJO itself may not significantly contribute to the subseasonal variability of EAWM precipitation. This same Rossby wave-train pattern along the subtropical jet, extending from the west coast of Europe all the way to North America, also tends to link the subseasonal variability of the EAWM to other densely populated extratropical regions, including WNA and MSR. These findings have important implications towards improved S2S prediction of winter extreme events worldwide, for example, over WNA, where a breakthrough in S2S prediction of winter precipitation is urgently needed to guide local water managers coping with recent prolonged droughts (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe leading subseasonal variability mode of precipitation associated with the EAWM\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo extract the leading subseasonal variability mode of precipitation associated with the EAWM, we applied an extended empirical orthogonal function (EEOF) analysis to the 10\u0026thinsp;\u0026minus;\u0026thinsp;90-day-filtered anomalous TRMM (Tropical Rainfall Measuring Mission) precipitation (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) during the extended boreal winter (November-April) from 1998 to 2016 over the East Asia region (18\u0026deg;‒37\u0026deg;N, 90\u0026deg;‒140\u0026deg;E; red box at day 0 in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), where strong local S2S variability in precipitation has been previously reported (e.g., 17, 19, 23). The leading subseasonal precipitation variability mode, with a prevailing period of about 25 days, is captured by the first pair of EEOF modes that are 90\u003csup\u003eo\u003c/sup\u003e out of phase (see Methods for details, also supporting Figs. S1 and S2); its evolution pattern can thus be depicted by lag-regression patterns of the 10\u0026ndash;90-day-filtered rainfall anomalies onto the time series of the principal component associated with the first leading EEOF mode (EPC\u003csub\u003e1\u003c/sub\u003e).\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, the leading subseasonal mode of precipitation over East Asia is characterized by a north-south dipole pattern of anomalous rain belts extending from Southeastern China to Japan/Korea with a gradual southeastward propagation with time. The peak positive precipitation anomalies over East Asia are observed at day 0, accompanied by southwesterly anomalous low-level winds converging onto the convection region, with an anticyclone to the east and a cyclone to the west of the enhanced precipitation center (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The southwesterly low-level winds, on one hand, bring rich moisture from southern China and the western Pacific to sustain vigorous convection; on the other hand, the upward motion induced by the quasi-geostrophic circulation can also play a crucial role in promoting enhanced convection over East Asia (to be elaborated in Fig.\u0026nbsp;2). Anomalous precipitation and circulation patterns at day \u0026minus;\u0026thinsp;12 largely mirror those at day 0 with an opposite sign, consistent with a period of about 25 days for the leading subseasonal mode of EAWM precipitation (supporting Fig. S2).\u003c/p\u003e \u003cp\u003eAssociated with the evolution of EAWM precipitation, the development of enhanced convection is observed over the western equatorial Indian Ocean (IO) near 60\u003csup\u003eo\u003c/sup\u003eE at day \u0026minus;\u0026thinsp;8; it then gradually intensifies and propagates eastward along the equator towards the Maritime Continent after day \u0026minus;\u0026thinsp;4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), exhibiting typical characteristics of the MJO. In association with strong precipitation over East Asia between day 0 and day 4, the enhanced MJO convection arrives over the eastern equatorial IO, corresponding to an MJO Phase 3 based on the Wheeler-Hendon MJO index (28, WH04). These results are largely in accord with many previous studies that emphasize the important role of the MJO in leading to the subseasonal variability of EAWM precipitation (e.g., 19, 21, 22, 23, 24, 25, 26).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRole of the tropical MJO for the subseasonal variability of EAWM precipitation\u003c/h3\u003e\n\u003cp\u003eTo quantify the contribution of the MJO to the leading subseasonal variability mode of EAWM precipitation, a regression-based approach is applied to derive daily precipitation and circulation anomalies associated with the MJO (see Methods). Evolution patterns of MJO-related precipitation and circulation anomalies associated with the leading subseasonal variability mode of EAWM precipitation can then be obtained by lag-regressions against the EPC\u003csub\u003e1\u003c/sub\u003e time series (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThe typical MJO evolution features associated with the subseasonal variations of EAWM precipitation are readily seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, including the initiation of an enhanced MJO convection over the western equatorial IO before day \u0026minus;\u0026thinsp;4, its intensification while slowly migrating eastward along the equator after day \u0026minus;\u0026thinsp;4, and crossing the Maritime Continent after day 4. The MJO-related precipitation and circulation anomalies, however, are largely confined to the tropics with rather weak signals over the EAWM region, suggesting that the MJO itself may not significantly contribute to the subseasonal variability of EAWM precipitation. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec further shows the evolution of the residual precipitation and circulation anomalies after the MJO signals are removed from the total, i.e., non-MJO signals associated with subseasonal precipitation variability over East Asia. The largely identical evolution features in precipitation and circulation anomalies over East Asia between Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec confirm the minor role of the MJO in driving the subseasonal variability of EAWM precipitation.\u003c/p\u003e \u003cp\u003eThis result largely supports the findings in Yao et al. (2015), which suggest that the MJO contributes to about 10% of subseasonal precipitation variability in East Asia. However, it contradicts many previous studies that underscore a crucial role of the MJO for the subseasonal variability of precipitation over the EAWM region. Differences are also noted between this study and Yao et al. (2015). While enhanced subseasonal precipitation over Southern China is found to be largely collocated with low-level cyclonic circulation in Yao et al. (2015; their Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), our analysis suggests that the anomalous positive precipitation center tends to be situated between an anticyclonic circulation to the east and a cyclonic circulation to the west (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, day 0). Moreover, the circumglobal Rossby-wave pattern associated with the subseasonal variability of EAWM precipitation, to be described below, was not evident in Yao et al. (2015), possibly due to an earlier generation of reanalysis dataset used in their study.\u003c/p\u003e\n\u003ch3\u003eTriggering of the MJO by circulation patterns associated with EAWM precipitation\u003c/h3\u003e\n\u003cp\u003eThe close association of enhanced subseasonal precipitation over East Asia with active MJO convection over the IO, but the minor role of the MJO in driving the subseasonal variability of EAWM precipitation, suggests that either the initiation of tropical MJO convection could be triggered by the subseasonal EAWM variability, or both the MJO and the subseasonal variability of EAWM precipitation are regulated by common large-scale factors. Triggering of tropical convection and the MJO by the subseasonal variability of the EAWM, such as cold surges, during its southward propagation has been previously reported (e.g., 17, 29, 30, 31), but mainly over the western Pacific and Maritime Continent regions. In contrast, the initiation of MJO convection as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb occurs over the western equatorial IO before day \u0026minus;\u0026thinsp;4. In this section, we will closely examine the detailed processes underlying MJO initiation over the western equatorial IO associated with the evolution of the subseasonal variability of EAWM precipitation.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;2a illustrates the time-longitude evolution of MJO-related precipitation anomalies along the equator based on lag-regression patterns shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb (shaded). The eastward propagation of the MJO associated with the subseasonal EAWM precipitation is clear, particularly over the Indian Ocean sector, with maximum precipitation anomalies located over the eastern IO over 80-100\u003csup\u003eo\u003c/sup\u003eE from day 0 to day 5. Consistent with the eastward propagation of convection, MJO-related 850hPa moisture anomalies also exhibit systematic eastward propagation over the IO (Fig.\u0026nbsp;2a, contours; see Methods for details on extraction of MJO-related anomalous fields), with a phase slightly leading precipitation anomalies by about 2\u0026ndash;3 days, signaling the well-known moisture preconditioning process during the MJO development. For example, prior to the initiation of active MJO convection over the western equatorial IO near 60\u003csup\u003eo\u003c/sup\u003eE between day \u0026minus;\u0026thinsp;10 and day \u0026minus;\u0026thinsp;5, enhanced MJO moisture anomalies start to emerge between day \u0026minus;\u0026thinsp;15 and day \u0026minus;\u0026thinsp;10 (Fig.\u0026nbsp;2a).\u003c/p\u003e \u003cp\u003eThe evolution of vertical moisture profiles associated with the MJO over the western IO is further illustrated in Fig.\u0026nbsp;2b (shaded), which is largely consistent with the evolution of anomalous upward motion associated with the MJO (Fig.\u0026nbsp;2c, shaded). Interestingly, the initiation of positive MJO moisture anomalies over the western IO between day \u0026minus;\u0026thinsp;15 and day \u0026minus;\u0026thinsp;10 (Fig.\u0026nbsp;2b, shaded) tends to closely follow non-MJO moisture anomalies that peak about 10 days earlier (Fig.\u0026nbsp;2b, contours), which are further closely linked to a maximum of non-MJO anomalous upward motion in the lower-troposphere between 850hPa and 500hPa (Fig.\u0026nbsp;2c contours), indicating that the initiation of the MJO over the western equatorial IO can be triggered by the non-MJO circulation anomalies.\u003c/p\u003e \u003cp\u003eTo further illustrate how the non-MJO circulation can initiate the MJO convection over the western equatorial IO, and considering the strongest signals in the non-MJO moisture (Fig.\u0026nbsp;2b) and vertical velocity (Fig.\u0026nbsp;2c) anomalies confined to the lower-troposphere, Fig.\u0026nbsp;2d presents anomalous non-MJO wind (vectors), moisture (shaded), and vertical velocity derived based on the quasi-geostrophic equation (QG\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\omega\\:\\)\u003c/span\u003e\u003c/span\u003e; 32, 33; contours) averaged between 850-500hPa and between day \u0026minus;\u0026thinsp;15 and day \u0026minus;\u0026thinsp;10. The non-MJO low-level moistening during this period, which can be critical for the MJO initiation as shown in Fig.\u0026nbsp;2b, is mainly evident over the Arabian Sea between the equator and 20\u003csup\u003eo\u003c/sup\u003eN, in the southeastern portion of a cyclonic circulation (Mark \u0026ldquo;A\u0026rdquo;) as a part of a wave-train linking to the EAWM. Enhanced moisture over this region can be ascribed to the QG upward motion over the eastern part of the cyclonic circulation (Fig.\u0026nbsp;2d, contours), which is largely induced by the warm temperature advection, and by the horizontal moisture advection due to southerly anomalous winds (figures not shown). In contrast, over the northwestern part of the cyclone, i.e., over northwestern India, the Mid-East, and East Africa, the prevailing non-MJO anomalous downward QG motion and northerly winds lead to drying of the lower troposphere.\u003c/p\u003e \u003cp\u003eA very similar cyclonic circulation pattern is also evident over East Asia at this time (\u0026ldquo;Mark B\u0026rdquo;, Fig.\u0026nbsp;2d), the dryness over the EAWM region associated with the downward QG vertical motion on the west of the cyclone is also consistent with a suppressed phase of the EAWM precipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, day \u0026minus;\u0026thinsp;12), in contrast to a wet condition and thus enhanced precipitation over the western North Pacific (WNP) to the east of the Philippines, in association with QG upward motion in the southeast of the cyclonic circulation. These results clearly suggest that the initiation of the MJO convection over the western equatorial IO and the subseasonal variability of EAWM precipitation are intimately linked to each other through the zonally elongated wave-train pattern.\u003c/p\u003e \u003cp\u003eWhile the extratropical origin of the MJO has been suggested by previous studies based on either case studies (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) or model experiments (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), results in this study clearly demonstrate the triggering of tropical MJO convection by a subtropical Rossby wave-train when passing over the northern Indian Ocean. While results in Fig.\u0026nbsp;2b suggest that the growth of the MJO moisture perturbations over the western IO tends to follow a peak phase of the non-MJO moisture and vertical velocity anomalies, the detailed processes underlying the phase lag between the MJO and non-MJO moisture anomalies need to be further understood. In addition to the thermodynamic processes, there is also the possibility that the extratropical Rossby wave disturbances centered over the Arabian Sea, as shown in Fig.\u0026nbsp;2d, could directly induce tropical Kelvin wave responses and thus trigger the initiation of MJO convection over the equatorial western IO, for example, through a \u0026ldquo;Sverdrup effect\u0026rdquo; as proposed by a recent idealized modeling study (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eThe global Rossby-wave pattern linking the EAWM with weather extremes worldwide\u003c/h3\u003e\n\u003cp\u003eConsidering the crucial role of the subtropical Rossby wave pattern in linking tropical MJO activity over the IO and the subseasonal variability of EAWM precipitation, the detailed characteristics of this subtropical Rossby wave-train are further examined in this section, particularly from a large-scale perspective. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the distribution of global 500hPa wind along with surface precipitation and temperature anomalies associated with the evolution of subseasonal EAWM precipitation. It is evident that the subtropical Rossby wave pattern over the Asian sector, which links the MJO initiation over the IO and the EAWM precipitation as previously discussed, is part of a global Rossby wave pattern with a wavenumber-6. This Rossby wave pattern extends from the North Atlantic, via the MSR, India, East Asia, the North Pacific, all the way to North America and the Gulf of Mexico, largely following a wave-guide along the subtropical westerly jet (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). It exhibits an eastward propagation with a phase speed of about 3 degrees per day over the Indo-Pacific sector (figure not shown). Given a wavelength of about 70 degrees of the Rossby wave train over the Asian sector, as roughly estimated by the distance between the two centers of cyclonic circulations (denoted by \u0026ldquo;A\u0026rdquo; and \u0026ldquo;B\u0026rdquo; in Fig.\u0026nbsp;2d), this propagation speed is largely in accord with the observed subseasonal period of about 25 days associated with EAWM precipitation variability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhile a close association between the Rossby wave pattern and precipitation over the EAWM region and the tropical Indo-Pacific region, as previously discussed, is again illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e (shaded), widespread impacts of this Rossby wave pattern on precipitation anomalies beyond the Indo-Pacific sector are readily discerned. These include the west coast of the Iberian Peninsula, MSR, the North Pacific, Alaska, WNA, the Gulf of Mexico, and even equatorial west Africa and the Amazon Basin in South America. For example, in association with the peak phase of the subseasonal EAWM precipitation at day 0, reduced precipitation anomalies are found near the MSR, over the north Pacific and Alaska, and the Gulf of Mexico, while enhanced precipitation is observed over the west coast of the Iberian Peninsula, WNA, and the Amazon Basin.\u003c/p\u003e \u003cp\u003eIn addition to precipitation, this circumglobal Rossby wave pattern also exerts significant influences on surface temperature (color dots in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For example, from day \u0026minus;\u0026thinsp;12 to day 0, associated with the transition of EAWM precipitation from a negative to a positive phase, a patch of cold surface temperature anomalies gradually intensifies and propagates southeastward from the MSR to the Mid-East/North Africa. Meanwhile, at day \u0026minus;\u0026thinsp;12 and day \u0026minus;\u0026thinsp;8, along with suppressed precipitation over East Asia, a so-called \u0026ldquo;Warm Arctic-Cold Continent (WACC)\u0026rdquo; pattern in surface temperature anomalies is discerned over North America. This WACC pattern has been reported as a leading subseasonal variability mode of wintertime surface temperature over North America and exerts significant modulations on regional temperature extremes and Arctic sea ice (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo illustrate the energy sources sustaining this subseasonal circumglobal Rossby wave pattern, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea illustrates the evolution of geopotential height (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\phi\\:}\\)\u003c/span\u003e\u003c/span\u003e) anomalies and wave activity fluxes (WAF; 43; see Methods) at 500hPa associated with the subseasonal EAWM precipitation. It is clear that the alternating positive and negative \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\phi\\:}\\)\u003c/span\u003e\u003c/span\u003e anomalies along the wave train are largely aligned along the subtropical jet stream wave-guide (contours in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Due to the presence of the Tibetan Plateau, the southward shift of the subtropical jet over the Indian sector creates a unique environment to interact with the tropics and initiate tropical MJO convection as discussed earlier.\u003c/p\u003e \u003cp\u003eAt day \u0026minus;\u0026thinsp;12, while the strongest \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\phi\\:}\\)\u003c/span\u003e\u003c/span\u003e anomalies and WAF are found over the North Pacific/North American sector, these anomalies are significantly weakened afterwards. At day \u0026minus;\u0026thinsp;8, wave disturbances over the Eurasian sector start to intensify, associated with the establishment of an anomalous high over the Iberian Peninsula and injection of strong WAF into the entrance region of the subtropical jet near North Africa and the MSR region. A strong divergence in WAF is seen over the west coast of the Iberian Peninsula at day \u0026minus;\u0026thinsp;8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), with rather weak wave fluxes from upstream, suggesting that the intensification of the Rossby wave-train downstream could be closely associated with the establishment of the anomalous high over the Iberian Peninsula. It is also noteworthy that the presence of positive \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\phi\\:\\)\u003c/span\u003e\u003c/span\u003e anomalies over both the Iberian Peninsula and the west North Atlantic near 60\u003csup\u003eo\u003c/sup\u003eW, and negative \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\phi\\:\\)\u003c/span\u003e\u003c/span\u003e anomalies over Iceland at day \u0026minus;\u0026thinsp;8, is strongly reminiscent of a positive phase of the North Atlantic Oscillation (NAO), indicating a potential role of the NAO in generating the circumglobal Rossby wave train along the subtropical jet stream. After day \u0026minus;\u0026thinsp;8, the eastward propagation of the Rossby-wave energy downstream along the subtropical jet is clearly discerned. At day 0, strong wave disturbances with a reversed sign to those at day \u0026minus;\u0026thinsp;12 re-emerge over the North Pacific/North America (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eEvolution of the Rossby wave pattern and associated WAF is further illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb by showing the vertical-longitude profiles of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\phi\\:}\\)\u003c/span\u003e\u003c/span\u003e anomalies and WAF averaged over the latitude belt between 25\u003csup\u003eo\u003c/sup\u003eN and 40\u003csup\u003eo\u003c/sup\u003eN, where the subtropical jet stream is largely situated over the Eurasian sector. The downstream propagation of Rossby-wave energy with a maximum around 250hPa, and a gradual eastward amplification of wave disturbances are again clearly seen. Particularly noteworthy that the rather weak WAF in the central North Pacific at day \u0026minus;\u0026thinsp;8 again suggests that processes associated with the establishment of the anomalous high over the Iberian Peninsula, a southern lobe of a positive NAO pattern, may play a critical role in energizing the Rossby wave train downstream along the subtropical jet.\u003c/p\u003e \u003cp\u003eNote that a zonally-extended Rossby wave pattern along the subtropical jet over the Eurasian sector during boreal winter has been widely reported in the variability of monthly or seasonal mean circulation anomalies (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), or on the synoptic time-scale associated with extreme precipitation and temperature events over East Asia (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). However, rather limited studies have focused on the subseasonal variability of the Rossby wave train along the subtropical Asian jet, particularly on its influence on global precipitation during the boreal winter season. While the subseasonal wave pattern along the subtropical jet can generate the subseasonal variability of EAWM precipitation as shown in this study, it is possible that the EAWM precipitation variability can also play a crucial role in triggering and maintaining these extratropical Rossby wave patterns as suggested by previous studies (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":" \u003cp\u003eDespite the urgent need for accurate S2S predictions to guide disaster preparedness and climate mitigation policy-making, our current S2S prediction skill remains rather limited, particularly for precipitation, partially due to a lack of understanding of key processes governing regional S2S variability. Focusing on the S2S variability of winter precipitation over East Asia in this study, we illustrate that subseasonal fluctuations of EAWM precipitation tend not only to be synchronized to MJO activity over the Indian Ocean, but also to be intimately linked to subseasonal variations of precipitation and surface temperature worldwide, through modulations by eastward-migrating Rossby wave-trains along the subtropical westerly jet. As further shown by Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e, beyond the tropical region, which features vigorous tropical convective variability, the strongest subseasonal variability of precipitation over the extratropics is largely observed along the latitudinal belt collocated with the circumglobal Rossby wave pattern. This is particularly evident over regions subject to further modulations by local topography along coastal areas, such as the west coast of the Iberian Peninsula, the coastal regions around MSR, the Bay of Bengal, Southeast China, and WNA.\u003c/p\u003e \u003cp\u003eParticularly, the southward displacement of the subtropical westerly jet over the Indian sector due to topographic impacts from the Tibetan Plateau creates a unique environment for this subtropical Rossby wave-train to interact with tropical convection. For instance, during the suppressed phase of subseasonal precipitation over the EAWM region, a lower-tropospheric trough is found over the Arabian Sea around 20\u003csup\u003eo\u003c/sup\u003eN as part of the Rossby wave-train (Fig.\u0026nbsp;2d, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Over the southeastern portion of the anomalous cyclonic circulation between the equator and 15\u003csup\u003eo\u003c/sup\u003eN, enhanced moisture anomalies are discerned, associated with the quasi-geostrophic anomalous ascending motion due to warm temperature advection and horizontal moisture advection associated with the southwesterly anomalous winds (Fig.\u0026nbsp;2d). Subsequently, MJO convection starts to initiate over the western Indian Ocean, then intensifies and propagates eastward along the equator. After about 10\u0026ndash;12 days, the previous cyclonic Rossby wave pattern over the Arabian Sea moves eastward over East Asia, promoting enhanced precipitation of the EAWM. Meanwhile, the MJO convection arrives over the eastern Indian Ocean, featuring a phase-lock between the enhanced subseasonal precipitation over East Asia and active MJO convection over the eastern Indian Ocean as previously reported. A decomposition of the total subseasonal EAWM precipitation into an MJO-related component and a non-MJO component, however, suggests that the MJO itself does not play a crucial role in the subseasonal EAWM precipitation. This contradicts many other studies that propose a crucial role of the MJO in the subseasonal variability of EAWM precipitation largely based on the aforementioned phase-lock relationship.\u003c/p\u003e \u003cp\u003eIn light of the profound influences of the circumglobal Rossby wave-train on the subseasonal variability of precipitation and temperature as illustrated in this study, these wave patterns can serve as critical predictability sources for S2S predictions over extensive regions along the subtropical westerly jet. For example, despite the severe long-lasting droughts over WNA in recent years, our state-of-the-art climate models exhibit rather limited skill for S2S prediction of winter precipitation over this region (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). The El Ni\u0026ntilde;o-Southern Oscillation (ENSO) has been used as a primary factor for seasonal prediction of winter precipitation over WNA, with wet (dry) conditions during El Ni\u0026ntilde;o (La Ni\u0026ntilde;a) winters (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). However, the unexpected extremely wet condition occurred over California during the 2022/2023 winter, was associated with a La Ni\u0026ntilde;a condition over the eastern Pacific, and was unpredictable by our major prediction systems (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Recent analyses indicate that the extremely wet condition over WNA during the 2022/2023 winter was closely associated with eastward-propagating Rossby wave disturbances from East Asia along the subtropical westerly jet (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e), as discussed in this study. The linkage of the cross-Pacific short Rossby wave-trains, which are independent from the El Ni\u0026ntilde;o, to the S2S variability of precipitation over WNA has also been reported in several recent studies (e.g., 12, 61, 62). This lends further evidence of the importance of the circumglobal Rossby wave-trains for S2S predictions of global weather and climate extremes.\u003c/p\u003e \u003cp\u003eResults in this study suggest that the establishment of the circumglobal Rossby wave train associated with the subseasonal variability of EAWM precipitation is closely associated with the formation of an anomalous high near the Iberian Peninsula, resembling a southern branch of the circulation anomalies during a positive phase of the NAO (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e, day \u0026minus;\u0026thinsp;8). Considering the MJO initiation over the Indian Ocean associated with the Rossby wave-train, this indicates a possible pathway of the NAO influences on the MJO over the Indian Ocean through the circumglobal subtropical Rossby waves. However, the positive NAO-like pattern about 8 days before the peak MJO convection over the eastern IO (i.e., Wheeler-Hendon MJO Phase 3) as suggested in this study is in contrast to the findings in previous studies (e.g., 63, 64), which reported that enhanced MJO convection over the Indian Ocean often follows a negative phase of the NAO pattern over the Atlantic sector. Further investigations are needed to fully understand these discrepancies. One possibility is that results shown in this study are associated with the subseasonal variability of EAWM precipitation; therefore, the NAO-like patterns and the MJO events captured in this study could only represent a small subset of total NAO and MJO events. For example, previous modeling studies suggested that the NAO condition is not necessary for exciting the circumglobal Rossby waves (e.g., 65, 66). It will be interesting to examine the percentage of MJO events over the IO initiated by the circumglobal subtropical Rossby waves, and the percentage of the circumglobal Rossby waves associated with the NAO.\u003c/p\u003e \u003cp\u003eWhile the subtropical circumglobal Rossby wave-train associated with subseasonal EAWM precipitation shown in this study largely exhibits similar characteristics to those previously reported associated with the monthly or seasonal mean anomalies, differences in the Rossby wave patterns on different time scales are also evident. For example, while a global wavenumber-5 structure of the circumglobal Rossby wave train has been reported based on monthly or seasonal anomalous fields during boreal winter (e.g., 45), a wavenumber-6 pattern is found in the subseasonal Rossby-wave train in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, also Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). As previously discussed, it is the unique wavelength (~\u0026thinsp;70\u003csup\u003eo\u003c/sup\u003e) and propagation phase speed (3 degrees per day) of the subseasonal Rossby wave pattern over the Indo-Pacific sector that determines a prevailing time-scale of about 25 days in winter precipitation over East Asia. Therefore, the large-scale factors determining the formation, amplitude, and phase of these subtropical circumglobal Rossby wave patterns on the subseasonal time scale, could also be different from those on monthly and seasonal time scales, which also warrants further investigations.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eObservational datasets\u003c/h2\u003e \u003cp\u003eDaily variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 reanalysis (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e) are used to characterize large-scale patterns associated with the subseasonal variability of precipitation over East Asia. These valuables include surface air temperature, 3D geopotential height (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\phi\\:}\\)\u003c/span\u003e\u003c/span\u003e), meridional and zonal winds (u, v), vertical velocity (w), and specific humidity (q) on 0.75\u0026deg;\u0026times;0.75\u0026deg; horizontal grids and 37 vertical pressure levels from 1000 to 1hPa.\u003c/p\u003e \u003cp\u003eDaily precipitation data from the Tropical Rainfall Measuring Mission (TRMM, version 3B42; 68) is used to extract the leading mode of the subseasonal variability of precipitation associated with the EAWM and the Madden-Julian Oscillation. Precipitation from the Global Precipitation Climatology Project (GPCP; 69) is used to characterize global precipitation anomalies associated with the leading subseasonal variability mode of EAWM considering its global coverage.\u003c/p\u003e \u003cp\u003eAnalyses in this study mainly focus on the extended boreal winter from November to March during the period of 1998\u0026ndash;2016. To extract the subseasonal variability signals, a 10\u0026thinsp;\u0026minus;\u0026thinsp;90-day band-pass filtering (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e) is applied to daily anomalies of each variable after removal of the climatological annual cycle (annual mean plus three leading harmonics).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe leading subseasonal variability mode of the East Asia winter monsoon\u003c/h3\u003e\n\u003cp\u003eInstead of an empirical orthogonal function (EOF) analysis used in many previous studies (e.g., 17), we employ an extended EOF (EEOF) analysis (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e) of 10\u0026ndash;90 day filtered daily TRMM precipitation anomalies during the 1998\u0026ndash;2016 winters (November-March) to identify the dominant subseasonal variability mode of EAWM precipitation. The EEOF is essentially the same as the EOF just using an extended covariance matrix with the daily data during all these time lags. The EEOF analysis is conducted over the region of 17.5\u0026deg;-35\u0026deg;N and 95\u0026deg;E-140\u0026deg;E (see the red box in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea at day 0) with a time lag of 31 days. The derived eigenvectors based on the EEOF analysis therefore contain a series of evolution patterns of the leading modes. As shown in Figs. S1 and S2, the first and second modes (EEOF\u003csub\u003e1\u003c/sub\u003e and EEOF\u003csub\u003e2\u003c/sub\u003e), with a same prevailing period of 25 days (0.04 cycle/day), explain about 4.9% and 4.7% of the total subseasonal precipitation variances, respectively, and thus represent the same leading subseasonal mode of the EAWM precipitation with a 90-degree phase difference. In this study, various large-scale variables associated with the evolution of the leading subseasonal precipitation mode over East Asia are derived by lag regressions of their corresponding 10-90-day filtered anomalies against the principal component of the EEOF\u003csub\u003e1\u003c/sub\u003e (EPC\u003csub\u003e1\u003c/sub\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of the MJO and its associated signals\u003c/h2\u003e \u003cp\u003eTo examine the role of the MJO in contributing to the subseasonal variability of EAWM precipitation, we decompose the total anomalous precipitation pattern associated with the subseasonal EAWM precipitation variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) into two components: precipitation anomalies associated with the MJO (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) and dependent from the MJO (the non-MJO component; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), with the non-MJO precipitation anomalies derived by subtracting the MJO component from the total precipitation anomalies.\u003c/p\u003e \u003cp\u003eSince we focus on the remote influences of MJO convection on the EAWM, and considering the caveat of the Wheeler-Hendon MJO index due to blended low-frequency variability signals (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e), we employ an EOF analysis of daily precipitation anomalies for 1998\u0026ndash;2016 winters (November-March) to identify the dominant MJO convective signals. The EOF analysis of 10-90-day filtered TRMM precipitation anomalies is conducted over the equatorial Indo-Pacific region, 15\u0026deg;S\u0026ndash;15\u0026deg;N, 40\u0026deg;\u0026ndash;160\u0026deg;E, where MJO convection is most active. The two leading EOF modes capture typical anomalous rainfall patterns of the MJO at their different phases during its eastward propagation, i.e., with enhanced MJO convection over the eastern equatorial Indian Ocean in EOF\u003csub\u003e1\u003c/sub\u003e and the Maritime Continent in EOF\u003csub\u003e2\u003c/sub\u003e (Fig. S3). The two leading principal components (PC\u003csub\u003e1\u003c/sub\u003e and PC\u003csub\u003e2\u003c/sub\u003e) are then used to derive typical patterns of any 2D or 3D variable (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{Z}\\)\u003c/span\u003e\u003c/span\u003e) associated with the two leading EOF modes of the MJO by regressing the daily 10-90-day filtered anomalies of the variable (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Z}}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e) on to PC\u003csub\u003e1\u003c/sub\u003e and PC\u003csub\u003e2\u003c/sub\u003e, and obtain their regressed patterns of RegZ\u003csub\u003e1\u003c/sub\u003e and RegZ\u003csub\u003e2\u003c/sub\u003e corresponding to the 1st and 2nd leading modes of the MJO, respectively. Anomalies of this variable associated with the MJO (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Z}}_{mjo}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e) on a particular day for 1998\u0026ndash;2016 winters can then be derived by the regression patterns normalized by the amplitude of PC\u003csub\u003e1\u003c/sub\u003e and PC\u003csub\u003e2\u003c/sub\u003e on that day, i.e.,\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Z}}_{mjo}^{{\\prime\\:}}\\)\u003c/span\u003e \u003c/span\u003e = RegZ\u003csub\u003e1\u003c/sub\u003e*PC\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;RegZ\u003csub\u003e1\u003c/sub\u003e*PC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eMeanwhile, anomalous fields that are independent of the MJO (i.e., the non-MJO component, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Z}}_{non\\_mjo}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e) can be obtained by subtracting the MJO-related components (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Z}}_{mjo}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e) from the total 10-90-day filtered anomalies (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Z}}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e), i.e., \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Z}}_{non\\_mjo}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Z}}^{{\\prime\\:}}-{\\text{Z}}_{mjo}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e. Note that a similar regression approach was used in Yao et al. (2015) to derive precipitation and circulation patterns associated with the MJO, but based on the WH04 MJO index.\u003c/p\u003e \u003cp\u003eThe evolution patterns of the variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{Z}\\)\u003c/span\u003e\u003c/span\u003e, as well as its MJO and non-MJO components, associated with the leading subseasonal variability mode of EAWM precipitation can be further derived by lag-regressions of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Z}}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Z}}_{mjo}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Z}}_{non\\_mjo}^{{\\prime\\:}}\\:\\)\u003c/span\u003e\u003c/span\u003eagainst the time series of EPC\u003csub\u003e1\u003c/sub\u003e, respectively, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for the precipitation and 800hPa winds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWave activity flux (WAF)\u003c/h2\u003e \u003cp\u003eAn analysis of the 3D wave activity flux (WAF) is conducted to explore possible energy sources in sustaining the subtropical Rossby wave rain associated with the subseasonal variability of EAWM precipitation. The calculation of 3D WAF is based on the W-vector following Takaya and Nakamura (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), which is expressed as\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\mathbf{W}=\\frac{\\text{p}}{2\\left|\\mathbf{U}\\right|}\\left\\{\\begin{array}{c}U\\left({{{\\psi\\:}}_{\\text{x}}^{{\\prime\\:}2}{-{\\psi\\:}}^{{\\prime\\:}}{\\psi\\:}}_{\\text{x}\\text{x}}^{{\\prime\\:}}\\right)+V\\left({{{{\\psi\\:}}_{\\text{x}}^{{\\prime\\:}}{\\psi\\:}}_{\\text{y}}^{{\\prime\\:}}{-{\\psi\\:}}^{{\\prime\\:}}{\\psi\\:}}_{\\text{x}\\text{y}}^{{\\prime\\:}}\\right)\\\\\\:U\\left({{{{\\psi\\:}}_{\\text{x}}^{{\\prime\\:}}{\\psi\\:}}_{\\text{y}}^{{\\prime\\:}}{-{\\psi\\:}}^{{\\prime\\:}}{\\psi\\:}}_{\\text{x}\\text{y}}^{{\\prime\\:}}\\right)+V\\left({{{\\psi\\:}}_{\\text{y}}^{{\\prime\\:}2}{-{\\psi\\:}}^{{\\prime\\:}}{\\psi\\:}}_{\\text{y}\\text{y}}^{{\\prime\\:}}\\right)\\\\\\:\\frac{{\\text{f}}_{0}^{2}}{{\\text{N}}^{2}}\\left[\\text{U}\\left({{{{\\psi\\:}}_{\\text{x}}^{{\\prime\\:}}{\\psi\\:}}_{\\text{z}}^{{\\prime\\:}}{-{\\psi\\:}}^{{\\prime\\:}}{\\psi\\:}}_{\\text{x}\\text{z}}^{{\\prime\\:}}\\right)+\\text{V}\\left({{{{\\psi\\:}}_{\\text{y}}^{{\\prime\\:}}{\\psi\\:}}_{\\text{z}}^{{\\prime\\:}}{-{\\psi\\:}}^{{\\prime\\:}}{\\psi\\:}}_{\\text{y}\\text{z}}^{{\\prime\\:}}\\right)\\right]\\end{array}\\right\\}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\psi\\:}}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e is the perturbation stream-function (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\psi\\:})\\)\u003c/span\u003e\u003c/span\u003e, derived based on lag-regressions of 10\u0026ndash;90 day filtered \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\psi\\:}}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e against the EPC\u003csub\u003e1\u003c/sub\u003e of the leading EAWM precipitation mode; U and V are winter mean zonal and meridional winds, respectively; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{f}}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the Coriolis parameter; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{N}}^{2}\\:\\)\u003c/span\u003e\u003c/span\u003eis the buoyancy frequency; p is the normalized pressure by 1000 hPa; and the subscripts represent partial derivatives in the corresponding x, y and z directions.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ERA5 data was downloaded from the website https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels. The daily GPCP precipitation data can be assessed at https://www.ncei.noaa.gov/data/global-precipitation-climatology-project-gpcp-daily/access/. The TRMM 3B42 rainfall data was downloaded from https://disc.gsfc.nasa.gov/datasets/TRMM_3B42_7/summary (DOI: 10.5067/TRMM/TMPA/3H/7).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is supported by the National Natural Science Foundation of China (42288101).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.-Y. X., X. J., and R. Z. designed research; J.-Y. X. carried out the analyses; and J.-Y. X., X. J., and R.Z. wrote the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIntergovernmental Panel on Climate Change, \u0026quot;Weather and Climate Extreme Events in a Changing Climate\u0026quot; in Climate Change 2021 \u0026ndash; The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\u003cem\u003e,\u003c/em\u003e C. Intergovernmental Panel on Climate, Ed. 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However, our latest climate models exhibit rather limited S2S prediction skill, particularly for precipitation, partially due to a lack of understanding of key processes governing regional S2S variability. In this study, we illustrate that the subseasonal variability of precipitation over the East Asian Winter Monsoon (EAWM) region is not only closely tied to activity of the Madden-Julian Oscillation (MJO) over the Indian Ocean, but also linked to precipitation and temperature extremes worldwide, influenced by a circumglobal Rossby wave-train along the subtropical westerly jet. Despite a close phase-lock relationship between the MJO and subseasonal EAWM precipitation, this study confirms a minor role of the MJO itself for the subseasonal EAWM precipitation, which contradicts many previous findings. Considering a crucial role of the circumglobal Rossby wave-train for S2S variability of global weather extremes, we call for significant community efforts towards improved understanding and predictions of the circumglobal Rossby wave-train. The implications of the underexploited predictability by this circumglobal Rossby wave-train for S2S prediction of winter precipitation over the west coast of North America are particularly discussed.\u003c/p\u003e","manuscriptTitle":"Linking the Subseasonal Variability of the East Asia Winter Monsoon and the Madden-Julian Oscillation through Wave Disturbances along the Subtropical Jet","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-12 09:16:31","doi":"10.21203/rs.3.rs-5337426/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-04T14:16:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-27T13:03:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-12T21:21:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87414052443944471812055102344185474462","date":"2024-11-21T17:00:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66469990699943416511304765737160527478","date":"2024-11-19T00:17:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-18T08:08:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-01T10:08:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-31T12:44:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Climate and Atmospheric Science","date":"2024-10-26T12:09:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-climate-and-atmospheric-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjclimatsci","sideBox":"Learn more about [npj Climate and Atmospheric Science](http://www.nature.com/npjclimatsci/)","snPcode":"41612","submissionUrl":"https://submission.springernature.com/new-submission/41612/3","title":"npj Climate and Atmospheric Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb58528c-3b4a-4fc3-8054-1b278fbf0c7b","owner":[],"postedDate":"November 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":39790458,"name":"Earth and environmental sciences/Climate sciences"},{"id":39790459,"name":"Earth and environmental sciences/Climate sciences/Atmospheric science"},{"id":39790460,"name":"Earth and environmental sciences/Climate sciences/Climate change"}],"tags":[],"updatedAt":"2025-05-19T16:08:49+00:00","versionOfRecord":{"articleIdentity":"rs-5337426","link":"https://doi.org/10.1038/s41612-025-01076-y","journal":{"identity":"npj-climate-and-atmospheric-science","isVorOnly":false,"title":"npj Climate and Atmospheric Science"},"publishedOn":"2025-05-16 15:58:07","publishedOnDateReadable":"May 16th, 2025"},"versionCreatedAt":"2024-11-12 09:16:31","video":"","vorDoi":"10.1038/s41612-025-01076-y","vorDoiUrl":"https://doi.org/10.1038/s41612-025-01076-y","workflowStages":[]},"version":"v1","identity":"rs-5337426","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5337426","identity":"rs-5337426","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}