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The Combined Effects of the Tropical and Extratropical Quasi-biweekly Oscillations on the Record-setting Mei-yu Rainfall in the Summer of 2020


doi: 10.1007/s00376-022-2050-1

  • During June–July 2020, the strongest recorded mei-yu rainfall occurred in the middle and lower reaches of the Yangtze River. The rainfall processes exhibited an obvious quasi-biweekly (biweekly in brief) variability, and there are altogether five cycles. It is found that the biweekly rainfall cycle mainly arises from the collaborative effects of biweekly variabilities from both the tropics and extratropics. As for the tropics, the biweekly meridional march and retreat of the western Pacific subtropical high (WPSH) is particularly evident. As for the extratropics, geopotential height anomalies near Lake Baikal are active. The former is attributed to the intensified biweekly activity of the southwest–northeast oriented East-Asian Pacific wave train (EAP) originating from the tropical western Pacific, while the latter is associated with the biweekly activities of the eastward propagating Eurasia mid-high latitudinal wave train and the westward propagating North Pacific wave train. Why the biweekly activities of these wave trains intensified is further diagnosed from the perspective of thermodynamical forcing and also from the modulation of interannual background on intraseasonal variability. It is found that the strongest recorded convection anchoring over the tropical western Indian Ocean (IO) triggers anomalous descent over the tropical western Pacific, which modulates the biweekly activity of the EAP. Meanwhile, the anomalous diabatic heating over the IO causes changes of the meridional thermodynamic contrast across the IO to the high latitudes, which modulates the extratropical wave trains. A further diagnosis of barotropic kinetic energy conversion suggests that the active occurrence of two extratropical biweekly wave trains is attributed to the increased efficiency of energy conversion from basic flow. The westward propagation of the extratropical North Pacific wave train is attributed to the weakened and north-shifted upper-level westerly, which is caused by the SST warmth near the Kuroshio extension.
    摘要: 2020年长江中下游梅雨破纪录异常增多,包括梅雨期在内的整个夏季经历了五次集中降水过程,表现出显著的准双周振荡特征。滤波分离出的准双周时间尺度和天气尺度降水分量均是1981年以来最强。本文基于观测分析,结合理想大气环流模式试验,对该年梅雨期准双周降水的成因进行了分析。结果表明,热带和中高纬大气准双周振荡的异常活跃,它们的协同作用是最强准双周降水发生的主因。在热带,存在西南-东北走向的印度洋-东亚准双周波列,以及沿着东亚沿岸传播的东亚-太平洋(EAP)波列。它们共同引起西太平洋副热带高压的准双周活动增强/减弱,导致从热带海洋向长江中下游水汽输送的准双周活动增强/减弱。在中高纬,欧亚大陆波列、北太平洋波列及准纬向传播的副热带欧亚波列的准双周振荡特征明显,它们导致贝加尔湖地区附近位势高度准双周活动渐弱/增强,有利向南入侵的干冷空气准双周活动增强/减弱。正是来自热带的暖湿气流与来自中高纬的干冷空气在长江中下游准双周交替交汇,导致了梅雨期强烈的准双周降水过程的发生。进一步分析表明,上述波列的准双周活动与背景海温异常有关。一方面,该年春夏热带西印度洋存在强烈的暖海温异常,该异常激发对流并局地锚定,增强了热带-北极经向热力梯度,增强大气斜压性与中亚西风急流,有利于背景气流向准双周波列的动能转换;同时,西印度洋暖海温也有利上空对流形成准双周振荡,激发海洋性大陆附近准双周下沉气流,引起EAP波列准双周振荡。此外,黑潮延伸区也存在暖海温异常。它引起经向热力梯度异常,使得气候态北太平洋西风急流出口处的西风向北偏移并减弱,减弱的背景西风和减小的北太平洋波列波数有利于波列西退,进而影响贝加尔湖地区位势高度场。
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  • Figure 1.  Spatial distribution of monthly accumulated precipitation (shading; units: mm) and its anomalies (contours; units: mm) relative to monthly climatological normal in (a) May, (b) June, (c) July, and (d) August 2020. The black dots represent the gauge stations, and the red boxes (28º–33ºN, 114º–120ºE) represent the MLYR region. The two landmarks, Dongting Lake and Poyang Lake, are indicated with small green dots.

    Figure 2.  The 2020 early summer (June–July) precipitation anomalies over the MLYR (the first row, units: mm) relative to climatological normal together with the rate (the second row, units: percentage) of precipitation components at different time scales ranging from 2–9 days (a, e), 10–20 days (b, f), 21–30 days (c, g), and 31–60 days (d, h).

    Figure 3.  (a) Wavelet power spectra of daily rainfall (contours) and (b) spectral analysis of the 5-day running mean daily precipitation (black line) over the MLYR from 1 June to 31 July 2020, and (c) temporal variation of daily precipitation (blue bars with the scale at the left axis, units: mm d–1) together with the 10–20-day filtered daily component (red line with the scale at the right axis, units: mm d–1). In (a), the shading indicates significance at the 95% confidence level, and the thick solid line denotes the cone above which the effects are important. In (b), red, green, and blue lines indicate the significance at 99%, 95%, and 90%, respectively. The dashed vertical lines indicate the beginning and ending dates of the twice biweekly events analyzed here.

    Figure 4.  (a) Time–latitudinal cross section of the 10–20-day filtered 850-hPa meridional wind anomalies (shading; units: m s–1) and horizontal wind anomalies (vectors; units: m s–1) averaged along 114º–120ºE. (b) Evolution of the ridgeline of the western Pacific subtropical high (WPSH) calculated as the averaged zero westerly from 110ºE to 150ºE (black line), together with its 10–20-day filtered component (green line), and (c) evolution of 500-hPa geopotential height anomalies (black line; unit: gpm) averaged over the region east to Lake Baikal (50º–70ºN, 110º–130ºE) together with its 10–20-day filtered component (green line; units: gpm). The red vertical dotted lines represent the peak wet phase of the two 10–20-day rainfall events indicated in Fig. 3c, respectively.

    Figure 5.  The 10–20-day filtered 850-hPa horizontal wind (vectors; units: m s–1) overlapped with the same filtered precipitation component (shading; unit: mm) for the dry (left column) and wet (right column) peak phase in the first biweekly event (a, b) and the second biweekly event (c, d), respectively.

    Figure 6.  As Fig. 5, but for Z500 anomalies (shading; units: gpm). The red line represents the contour of climatological 5880 gpm, which is used to indicate the location of the WPSH, while the black line represents the contour of 5880 gpm during the peak wet or dry phase of the biweekly events.

    Figure 7.  Composite of the 500-hPa stream function anomalies (shading; units: 106 m s–2) of the two biweekly intraseasonal rainfall events, in which 0d represents the extreme wet phase, and –6d to 4d represents the leading days of the wet phase. The four colorful lines represent the four wave trains, which are further seen in the time cross section below.

    Figure 8.  Temporal cross section of 10–20-day filtered Z500 (shading; units: gpm) along the four wave trains indicated in Fig. 7. (a) is for the mid-high latitudinal Eurasian wave train (red line in Fig. 7), (b) is for the subtropical Eurasian wave train (pink line in Fig. 7), (c) is for the EAP (purple line in Fig. 7), and (d) is for the North Pacific wave train (green line in Fig. 7) during 2020 summer. The thick black arrows indicate the propagation direction of the wave trains.

    Figure 9.  The time–longitudinal cross section of (a) original OLR over the tropics averaged from 15ºS–15ºN and (b) its 10–20-day filtered component (shading, units: W m–2), together with the spectral analysis of OLR (c) over the tropical Indian Ocean (15ºS–15ºN, 75º–90ºE) and (d) over the tropical western Pacific (15ºS–15ºN, 110º–125ºE) during early summer 2020. (e) The phase space diagram of the MJO, with black, red, green, and blue indicating May, June, July, and August, individually.

    Figure 10.  Composites of early summer OLR anomalies (shading, units: W m–2) based on the years with (a) enhanced and (b) weakened biweekly precipitation. Blue boxes indicate the area of the western Indian Ocean (40º–80ºE, 20ºS–20ºN) with significant convection anomalies.

    Figure 11.  Scatterplots of the Indian Ocean OLR anomaly index with (a) the quasi-biweekly precipitation index over the MLYR (QBW-rain), (b) the quasi-biweekly EAP intensity index (QBW_EAP), and (c) the quasi-biweekly Z500 index near Lake Baikal [QBW-Z500 (bkl)]. The blue (red) dots represent the years with stronger (weaker) quasi-biweekly precipitation, while the asterisk indicates year 2020.

    Figure 12.  Horizontal distribution of barotropic energy conversion of quasi-biweekly fluctuations (10–5 m2 s–3) at 250 hPa for (a) summer 2020, (b) the climatological mean, and (c) the difference of (a) minus (b).

    Figure 13.  Horizontal distribution of U200 (m s–1) for (a) summer 2020, (b) the climatological mean, and (c) the difference of (a) minus (b).

    Figure 14.  Schematic illustrating that the biweekly wave trains converge over the MLYR to induce the strongest recorded rainfall in June–July 2020. The four black arrowed lines indicate the quasi-biweekly wave trains. In the upper panel, the red (black) contour indicates the climatological (observed in 2020) 5880-gpm contour of Z500, and shading indicates Z500 anomalies. In the lower panel, the red (blue) arrow represents anomalous warm and wet (cold and dry) flows originating from the South China Sea and Bay of Bengal (the north), and shading represents seasonal OLR anomalies.

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Manuscript received: 25 February 2022
Manuscript revised: 08 August 2022
Manuscript accepted: 25 August 2022
通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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The Combined Effects of the Tropical and Extratropical Quasi-biweekly Oscillations on the Record-setting Mei-yu Rainfall in the Summer of 2020

    Corresponding author: Shuanglin LI, shuanglin.li@mail.iap.ac.cn
  • 1. Department of Atmospheric Science/Centre for Severe Weather and Climate and Hydro-geological Hazards, China University of Geosciences, Wuhan 430074, China
  • 2. China Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 3. State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing 100081, China

Abstract: During June–July 2020, the strongest recorded mei-yu rainfall occurred in the middle and lower reaches of the Yangtze River. The rainfall processes exhibited an obvious quasi-biweekly (biweekly in brief) variability, and there are altogether five cycles. It is found that the biweekly rainfall cycle mainly arises from the collaborative effects of biweekly variabilities from both the tropics and extratropics. As for the tropics, the biweekly meridional march and retreat of the western Pacific subtropical high (WPSH) is particularly evident. As for the extratropics, geopotential height anomalies near Lake Baikal are active. The former is attributed to the intensified biweekly activity of the southwest–northeast oriented East-Asian Pacific wave train (EAP) originating from the tropical western Pacific, while the latter is associated with the biweekly activities of the eastward propagating Eurasia mid-high latitudinal wave train and the westward propagating North Pacific wave train. Why the biweekly activities of these wave trains intensified is further diagnosed from the perspective of thermodynamical forcing and also from the modulation of interannual background on intraseasonal variability. It is found that the strongest recorded convection anchoring over the tropical western Indian Ocean (IO) triggers anomalous descent over the tropical western Pacific, which modulates the biweekly activity of the EAP. Meanwhile, the anomalous diabatic heating over the IO causes changes of the meridional thermodynamic contrast across the IO to the high latitudes, which modulates the extratropical wave trains. A further diagnosis of barotropic kinetic energy conversion suggests that the active occurrence of two extratropical biweekly wave trains is attributed to the increased efficiency of energy conversion from basic flow. The westward propagation of the extratropical North Pacific wave train is attributed to the weakened and north-shifted upper-level westerly, which is caused by the SST warmth near the Kuroshio extension.

摘要: 2020年长江中下游梅雨破纪录异常增多,包括梅雨期在内的整个夏季经历了五次集中降水过程,表现出显著的准双周振荡特征。滤波分离出的准双周时间尺度和天气尺度降水分量均是1981年以来最强。本文基于观测分析,结合理想大气环流模式试验,对该年梅雨期准双周降水的成因进行了分析。结果表明,热带和中高纬大气准双周振荡的异常活跃,它们的协同作用是最强准双周降水发生的主因。在热带,存在西南-东北走向的印度洋-东亚准双周波列,以及沿着东亚沿岸传播的东亚-太平洋(EAP)波列。它们共同引起西太平洋副热带高压的准双周活动增强/减弱,导致从热带海洋向长江中下游水汽输送的准双周活动增强/减弱。在中高纬,欧亚大陆波列、北太平洋波列及准纬向传播的副热带欧亚波列的准双周振荡特征明显,它们导致贝加尔湖地区附近位势高度准双周活动渐弱/增强,有利向南入侵的干冷空气准双周活动增强/减弱。正是来自热带的暖湿气流与来自中高纬的干冷空气在长江中下游准双周交替交汇,导致了梅雨期强烈的准双周降水过程的发生。进一步分析表明,上述波列的准双周活动与背景海温异常有关。一方面,该年春夏热带西印度洋存在强烈的暖海温异常,该异常激发对流并局地锚定,增强了热带-北极经向热力梯度,增强大气斜压性与中亚西风急流,有利于背景气流向准双周波列的动能转换;同时,西印度洋暖海温也有利上空对流形成准双周振荡,激发海洋性大陆附近准双周下沉气流,引起EAP波列准双周振荡。此外,黑潮延伸区也存在暖海温异常。它引起经向热力梯度异常,使得气候态北太平洋西风急流出口处的西风向北偏移并减弱,减弱的背景西风和减小的北太平洋波列波数有利于波列西退,进而影响贝加尔湖地区位势高度场。

    • The mei-yu rainfall in the middle and lower reaches of the Yangtze River (MLYR), China, is usually a result of the seasonal collision and convergence of warm-wet air flow from the south with cold-dry air from the north in early June to mid July (Ninomiya, 2000; Ding, 2007). Climatologically, with the seasonal northward march of the East Asian summer monsoon (EASM) in early–middle June, the Western Pacific Subtropical High (WPSH) shifts northward with the ridgeline (i.e., zero westerly component averaged between 110°E to 150°E) reaching about 25°N, thus anchoring the convergence of strong warm-wet vapor with cold-dry air mass from the north and causing the onset of the mei-yu front with heavy rainfall processes over the MLYR (Tao and Chen, 1987; Tanaka, 1992; Chen et al., 2000; Ding and Chan, 2005; Li et al., 2015; Sun et al., 2019). Accompanied with the further northward march of the EASM in mid July, the ridgeline of the WPSH shifts further northward to 30°N, pushing the convergence center of the warm-wet and cold-dry air masses to North China or Northeastern China. As a consequence, the mei-yu rainfall is terminated, and the North China rainy season begins.

      During June–July 2020, the mei-yu rainfall was extremely enhanced (Fig. 1). The accumulated precipitation over the basin of Poyang Lake, one key subregion of the MLYR, exceeded 900 mm, which is more than twice the normal amount and breaks the record held since 1961 (Liu et al., 2020; Ding et al., 2020). Meanwhile, the mei-yu event duration was 52 days in total, which is 23 days longer than normal and ranks as the 5th longest mei-yu event since 1961 (Wang et al., 2020). The longer-lasting and record-breaking intensified Meiyu rainfall triggered severe floods over the MLYR and Japan and caused devastating damage to properties (Takaya et al., 2020). Therefore, understanding the multiscale variability of the anomalous mei-yu event in 2020 and revealing the mechanisms for its occurrence is important for improving climate prediction and for warning on and mitigating disasters.

      The mei-yu rainfall in 2020 exhibited a substantial intraseasonal characteristic, with a total of five heavy precipitation processes, as seen from Wang et al. (2020) and from the present analysis. This is somewhat similar to previous cases such as years 1991, 1998, and 2016 (Chan et al., 2002; Zhou and Chan, 2005; Mao and Wu, 2006; Mao et al., 2010; Ren et al., 2013; Yang et al., 2014; Shao et al., 2018; Huang et al., 2020). Mao and Wu (2006) investigated the role of intraseasonal activities in the 1991 case and identified a strong periodicity of 15–35 days. Zhu et al. (2003) analyzed the 1998 case and found a feature of 30–60-day oscillation. Shao et al. (2018) analyzed the 2016 case and showed an intraseasonal periodicity similar to 1998. These studies reveal that the causes of intraseasonal precipitation variability are different from case to case. The 2020 case features a shorter periodicity of nearly two weeks. However, what causes the biweekly fluctuation of precipitation in the 2020 case is unclear.

      It is well known that rainfall processes are governed by the combined effects of various atmospheric circulation systems. For a persistent rainfall process beyond synoptic scale, stable large-scale atmospheric circulation systems provide a favorable background. The intraseasonal oscillations (ISOs) of atmospheric circulation systems are the important triggers of persistent rainfall processes. Generally, the ISOs causing the intraseasonal variabilities of summer rainfall may arise from the tropical convection, or the extratropical atmospheric intrinsic variability, or both (Chan et al., 2002; Zhou and Chan, 2005; Zhou et al., 2006; Mao and Wu, 2006; Ren et al., 2013; Yang et al., 2014). For instance, Mao et al. (2010) demonstrated that the 1991 case may be linked to tropical intraseasonal variabilities over the WNP propagating northward or northwestward. Zhu et al. (2003) emphasized the role of ISOs originating from the South China Sea and propagating northward toward the MLYR. Yang et al. (2010) illustrated the importance of the mid–high-latitudinal intraseasonal variabilities since they carry cold-dry air masses to converge with the warm-wet flows from the south. Zhang et al. (2008) illustrated the combined effects of the eastern Tibetan Plateau intraseasonal anomalous cyclone (anticyclone) and the western subtropical Pacific anticyclone (cyclone) in the upper troposphere. These analyses suggest no common mechanism of ISOs between different mei-yu cases.

      As shown in a later section, like the total mei-yu precipitation, the biweekly component of precipitation in the 2020 case is also the strongest on record. Determining the fraction contributed by the biweekly precipitation component to the total precipitation requires further investigation. Also, the regulating factors for the intraseasonal evolution of rainfall are unclear. And whether or not, and how the biweekly signals from both the tropics and the extratropics combined to affect the intraseasonal variability in 2020 have yet to be shown. These questions are the motivation of the present study.

      The remainder of this paper is organized as follows. Section 2 describes the datasets and methods used in this study. Section 3 presents the 2020 case analysis, in which the intraseasonal rainfall variability is demonstrated first; the variations of two primary biweekly circulation systems, the south–north shift of the WPSH and the alternative emergence of geopotential height anomalies near Lake Baikal, are illustrated next; then, the factors affecting the biweekly activities of these two circulation systems are analyzed. Section 4 addresses the formation mechanisms for the biweekly rainfall variabilities, particularly the role of the intensified and anchored convection over the tropical IO. Then, section 5 examines the occurrence mechanism of midlatitude biweekly wave trains and the westward retreat of the North Pacific wave train. Finally, conclusions and discussions are provided in section 6.

    2.   Datasets and methods
    • Daily instrumental rainfall records at 774 stations on the Chinese mainland (dots in Fig. 1) are provided by the National Meteorological Information Center, China Meteorological Administration (CMA). Daily horizontal winds and geopotential heights are obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis-1 (Kalnay et al., 1996). Outgoing longwave radiation (OLR) data are provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, and downloaded from https://www.esrl.noaa.gov/psd/ (Liebmann and Smith, 1996). The horizontal resolution of the NCEP/NCAR reanalysis and the OLR datasets is 2.5° × 2.5°. The Real-time Multivariate MJO (RMM) index (Wheeler and Hendon, 2004) used to determine the amplitude and phase of the MJO is obtained from the Australian Bureau of Meteorology (http://www.bom.gov.au/climate/mjo/). Climatological normal is derived from the mean of 1981 to 2020, and the mei-yu summer denotes June–July.

      To isolate the intraseasonal components, a 101-point, 5–100-day, band-pass filtering technique is applied to the daily anomalies with the seasonal daily cycle removed at each grid point by subtracting the first three harmonics of the calendar mean for each day, as in Wheeler and Hendon (2004). The basic methods used include composite, correlation, and regression analyses.

      To examine the source of biweekly wave trains, the barotropic kinetic energy conversion (CKiso) (Hoskins et al., 1983) is calculated as follows:

      where an overbar denotes the early summer mean state, and a prime is the biweekly perturbation. A positive value of CKiso represents energy conversion from the mean flow to the biweekly perturbation.

    3.   Rainfall, circulation, and wave activities in the 2020 case
    • Figure 1 shows the distribution of monthly accumulated rainfall. In May, the primary rainband is situated in southeast China, with several centers over the south Yangtze River valley and south China (Fig. 1a). In June, there are two concentrated precipitation regions situated over the MLYR and the southwestern region of South China, respectively (Fig. 1b). In July, heavy rainfall is situated over the MLYR, with the maximum exceeding 650 mm (Fig. 1c). In August, there is evenly distributed rainfall over the MLYR, and the heavy rainfall center shifts to the western side of the Sichuan Basin (Fig. 1d). Compared with the climatological normal, the precipitation in June and July increases significantly, which makes the early summer 2020 ranking the first in the 40 years from 1981 to 2020 (Fig. S1 in the electronic supplementary materials). Specifically, intensified precipitation is seen across the whole MLYR basin, with the intensity being more than doubled in some subregions (Fig. S2 in the ESM). In the Poyang Lake basin, precipitation anomalies even exceed 400 mm in July (Fig. 1c). Such a significant variation of heavy rainfall centers suggests a substantial intraseasonal variability.

      Figure 1.  Spatial distribution of monthly accumulated precipitation (shading; units: mm) and its anomalies (contours; units: mm) relative to monthly climatological normal in (a) May, (b) June, (c) July, and (d) August 2020. The black dots represent the gauge stations, and the red boxes (28º–33ºN, 114º–120ºE) represent the MLYR region. The two landmarks, Dongting Lake and Poyang Lake, are indicated with small green dots.

      To explore the periodic characteristic, Fig. 2 displays the total rainfall anomalies along with the proportions of precipitation components at four different time scales ranging from 2–9 days (synoptic), 10–20 days (biweekly), 21–30 days (quasi-monthly), and 31–60 days (typical MJO-like). We can see that the component of synoptic time scale is dominant, accounting for over 40% of the total rainfall (Figs. 2a and 2e). In the MLYR and as far east as 114ºE, the biweekly component is substantially strong and accounts for about 20% of the total (Figs. 2b and 2f). Moreover, over the Poyang Lake region, the biweekly component value exceeds 300 mm (Fig. 2b). Where the biweekly component is larger, the 2–9-day component accounts for a relatively smaller proportion (Figs. 2e and 2f). Overall, the components in 21–30 days and 31–60 days are small (Figs. 2g and 2h). That is, for the intraseasonal time scale, biweekly precipitation is dominant.

      Figure 2.  The 2020 early summer (June–July) precipitation anomalies over the MLYR (the first row, units: mm) relative to climatological normal together with the rate (the second row, units: percentage) of precipitation components at different time scales ranging from 2–9 days (a, e), 10–20 days (b, f), 21–30 days (c, g), and 31–60 days (d, h).

      The biweekly precipitation component in 2020 is the strongest in the past 40 years, as seen from the evolution comparison of rainfall components through 1981–2020 with different time scales ranging from synoptic (2–9 days), bi-weekly (10–20 days), quasi-monthly (21–30 days), and low-frequency intraseasonal (31–60 days), respectively (Fig. S3 in the ESM). Evidently, the biweekly component in 2020 is the strongest on record with a value of 170 mm, which is nearly twice as much as the climatological biweekly component (Fig. S3b). In addition, one may note the synoptic component in 2020 (Fig. S3a) is also the strongest on record with a value of 400 mm.

      This biweekly characteristic in 2020 is verified by the wavelet analysis and spectral analysis. The most significant wavelet periodicity is around two weeks and occurs mainly in July (Fig. 3a). Also, the 10–20-day periodicity is dominant in the 5-day running mean precipitation from 1 May to 31 August (Fig. 3b), significant at the 99% level. These biweekly features are even clearer in the evolution of daily rainfall, particularly in its biweekly filtered component (Fig. 3c).

      Figure 3.  (a) Wavelet power spectra of daily rainfall (contours) and (b) spectral analysis of the 5-day running mean daily precipitation (black line) over the MLYR from 1 June to 31 July 2020, and (c) temporal variation of daily precipitation (blue bars with the scale at the left axis, units: mm d–1) together with the 10–20-day filtered daily component (red line with the scale at the right axis, units: mm d–1). In (a), the shading indicates significance at the 95% confidence level, and the thick solid line denotes the cone above which the effects are important. In (b), red, green, and blue lines indicate the significance at 99%, 95%, and 90%, respectively. The dashed vertical lines indicate the beginning and ending dates of the twice biweekly events analyzed here.

      As Chan et al. (2002) illustrated, a complete ISO cycle includes an active (wet phase) and a break (dry phase) period, and both have a peak amplitude. From the filtered biweekly rainfall components (Fig. 3c), there are a total of five cycles. Two cycles are significant and pass the significance test, one spanning from 30 June to 11 July with the wet (dry) phase peaking on 6 July (30 June) and the other spanning from 12 to 24 July with the wet (dry) phase peaking on 18 July (12 July). Since these two cycles are closely connected to the mei-yu rainfall, they are the focus of the following subsection.

    • To describe the evolution of air flows, a time–latitudinal cross section of the 10–20-day filtered wind anomalies at 850 hPa is shown in Fig. 4a. Basically, in the low latitudinal region of about 25ºN, there is a center of meridional wind anomaly, which experiences five oscillations. Meanwhile, over the extratropics, north of 40ºN, there is another center which also oscillates five times. Along with the beginning of the two precipitation events (the first black dotted line in Fig. 4a), the extratropical center jumps northward to about 50ºN. All five alternations of mei-yu wet and dry phases correspond well to the transitions of biweekly meridional wind anomaly. When the MLYR is controlled by the anomalous northerly or southerly flow, precipitation enters a dry phase (Figs. 3c and 4a). Only when both the anomalous cold-dry northerly flow and warm-wet southerly flow reach at the same time and converge over the MLYR does precipitation enter a wet phase (the red lines in Fig. 4a). In short, the biweekly alternation of abnormal meridional wind corresponds to a shift of the dry-wet phase of precipitation. The following analysis aims to identify the systems which regulate the biweekly variation of the two meridional wind anomalies.

      Figure 4.  (a) Time–latitudinal cross section of the 10–20-day filtered 850-hPa meridional wind anomalies (shading; units: m s–1) and horizontal wind anomalies (vectors; units: m s–1) averaged along 114º–120ºE. (b) Evolution of the ridgeline of the western Pacific subtropical high (WPSH) calculated as the averaged zero westerly from 110ºE to 150ºE (black line), together with its 10–20-day filtered component (green line), and (c) evolution of 500-hPa geopotential height anomalies (black line; unit: gpm) averaged over the region east to Lake Baikal (50º–70ºN, 110º–130ºE) together with its 10–20-day filtered component (green line; units: gpm). The red vertical dotted lines represent the peak wet phase of the two 10–20-day rainfall events indicated in Fig. 3c, respectively.

      In the middle troposphere, the ridgeline of the WPSH, which can be expressed as the location of zero westerly component averaged from 110ºE to 150ºE, experienced five alternations (black solid line in Fig. 4b) as the biweekly rainfall (Fig. 3c). The beginning of the wet phases corresponds to the date when the WPSH shifts southward, which can be explained as the effect of the outbreak of cold-dry air from the north. At the mid-high latitudes, the 500-hPa geopotential heights (Z500) east to Lake Baikal also experience five alternations (Fig. 4c), with the negative phase corresponding to the wet phase. One calculation suggests that the 10–20-day filtered Z500 averaged in the region (50º–70ºN, 110º–130ºE) (Fig. 4c) is significantly and negatively correlated with the biweekly precipitation (–0.71, Fig. 3a). This is intuitive since lower Z500 values correspond to a trough and act as the guidance for the movement and outbreak of cold-dry air mass.

      Figure 5 compares the filtered 850-hPa wind (vectors) and precipitation (shading) in the dry phase with those in the wet phase of the two biweekly events. During the wet phase, corresponding to the southwestward shift of the WPSH (Figs. 6b and 6d), anomalous lower-level southwesterly flow initiates over the South China Sea (Figs. 5b and 5d); there are negative Z500 anomalies over Eastern Siberia northeast to Lake Baikal (Figs. 6b and 6d). The lower-level cold-dry air is guided and transported (Figs. 5b and 5d) to converge over the MLYR with the warm-wet air flows from the South China Sea, favoring the mei-yu rainfall.

      Figure 5.  The 10–20-day filtered 850-hPa horizontal wind (vectors; units: m s–1) overlapped with the same filtered precipitation component (shading; unit: mm) for the dry (left column) and wet (right column) peak phase in the first biweekly event (a, b) and the second biweekly event (c, d), respectively.

      Figure 6.  As Fig. 5, but for Z500 anomalies (shading; units: gpm). The red line represents the contour of climatological 5880 gpm, which is used to indicate the location of the WPSH, while the black line represents the contour of 5880 gpm during the peak wet or dry phase of the biweekly events.

      In comparison, during the dry phase (Figs. 5a and 5c), anomalous lower-level northeasterly flow prevails over the MLYR, which guides dry-cold air to move further southward to south China and suppresses precipitation over the MLYR. The two events are similar overall, except for a northward shift of the convergence center in the second event. The latter event is consistent with the more northward location of the subtropical high (Fig. 4b) and with the northward shift of the precipitation center (Figs. 5b and 5d). Thus, the biweekly variations of the WPSH and Z500 near Lake Baikal are crucial for the occurrence of the biweekly precipitation. The causes of their biweekly variations are investigated in the following subsection.

    • Previous studies have illustrated that there are five branches of various wave trains that affect mei-yu rainfall, including the two mid-high latitudinal arched wave trains propagating eastward over the Eurasian Continent or propagating westward from the North Pacific (Hsu and Lin, 2007; Hu et al., 2016; Ren et al., 2020), the Silk Road wave train propagating along the subtropical jet (Lu et al., 2002; Ding and Wang, 2007), the meridional wave train originating from the tropical IO and extending toward the Tibetan Plateau and further stretching to East Asia (IO–TP–EA) (Zhang et al., 2009), and the East Asia–Pacific or Pacific–Japan wave train (EAP/PJ) (Nitta, 1987; Huang, 1992; Kosaka et al., 2013; Sun et al., 2020). To illustrate the potential roles of these wave trains, the composite evolutions of 500-hPa streamfunction anomalies for the two events studied here are displayed in Fig. 7.

      Figure 7.  Composite of the 500-hPa stream function anomalies (shading; units: 106 m s–2) of the two biweekly intraseasonal rainfall events, in which 0d represents the extreme wet phase, and –6d to 4d represents the leading days of the wet phase. The four colorful lines represent the four wave trains, which are further seen in the time cross section below.

      On day –6 (Fig. 7a), there are positive streamfunction anomalies northeast to Lake Baikal, along with negative anomalies extending through central-eastern China to the southeast of Japan, as seen in Fig. 6a. Meanwhile, there are negative anomalies upstream over Europe and positive anomalies further upstream over the Atlantic. These anomalies together constitute an arched wave-train-like structure extending from the North Atlantic to East Asia (marked with a red line in Fig. 7a), reminiscent of the mid-high latitudinal Eurasian wave train. Meanwhile, there is a quasi-zonal chain of anomalies along 40ºN, reminiscent of the Silk Road pattern (marked with a pink line in Fig. 7a). On day –4, there is a chain of anomalies from the North Pacific to East Asia, with two negative centers, one over the Okhotsk Sea and the other over the central-eastern North Pacific, along with two positive centers over central Russia and the Gulf of Alaska. These anomalies also form an arched structure (marked with a green line in Fig. 7c), reminiscent of the mid-high latitudinal North Pacific wave train (Du and Lu, 2021). Additionally, at day 0, there is a meridional chain of anomaly centers originating from the western Pacific, reminiscent of the EAP/PJ wave train (marked with a purple line).

      To quantify the propagation of these wave train structures, we analyze the temporal cross section of biweekly filtered Z500 along the wave train axis, which is marked with dots in Fig. 7a. From Fig. 8, the eastward (westward) propagation of the mid-high latitudinal Eurasian (North Pacific) wave train (Figs. 8a and 8d) and the northeastward propagation of the EAP/PJ (Fig. 8c) can be seen. From the timing of these wave train propagations, albeit with various directions and origins, their propagations toward the East Asia coastal region are common. They meet over northern East Asia, forming strong negative Z500 anomalies around Lake Baikal. Thus, it is the biweekly activity of these wave trains that modulates the biweekly variation of geopotential heights near Lake Baikal and the WPSH.

      Figure 8.  Temporal cross section of 10–20-day filtered Z500 (shading; units: gpm) along the four wave trains indicated in Fig. 7. (a) is for the mid-high latitudinal Eurasian wave train (red line in Fig. 7), (b) is for the subtropical Eurasian wave train (pink line in Fig. 7), (c) is for the EAP (purple line in Fig. 7), and (d) is for the North Pacific wave train (green line in Fig. 7) during 2020 summer. The thick black arrows indicate the propagation direction of the wave trains.

    4.   Roles of IO convection in modulating the biweekly rainfall
    • Studies have shown that IO convection can not only directly excite the wave train emanating from the IO to cross the Tibetan Plateau to reach East Asia (IO–TP–EA; Zhang et al., 2009), but also force an anomalous cyclone or anticyclone and convection anomaly near the Philippine Seas through the Walker Cell. The latter modulates the EAP (Sun et al., 2016; Xie et al., 2016). Additionally, the anomalous tropical diabatic heating associated with the IO convection may modify the meridional thermal contrast between the tropics and the high latitudes, change the atmospheric background flow, and further modulate the intraseasonal activity of wave trains (Ding and Wang, 2007; Zhang et al., 2019b). Thus, the IO convection anomalies may have played a substantial role in regulating the above wave trains.

    • Figure 9 shows the daily evolution of OLR anomaly over the tropics (15ºS–15ºN) and its biweekly component in summer 2020. There are five oscillation cycles (Fig. 9a), and the biweekly variation is preliminarily apparent from Fig. 9b. A spectral analysis of OLR anomaly averaged over the IO shows a significant biweekly period of about 12 days (Fig. 9c). A similar biweekly period is also seen in the OLR anomaly averaged over the tropical western Pacific region (15ºS–15ºN, 110º–125ºE) (Fig. 9d). Consistently, there is anomalous descent over the Philippines along with ascent over the tropical IO (Fig. S4 in the ESM). This verifies the seesaw-like variation between the IO and western Pacific convection. Thus, the anomalous IO convection may have modulated the biweekly activity of the EAP/PJ wave train through affecting the western Pacific convection. Considering that the IO convection can directly trigger the IO–TP–EA wave train (Zhang et al., 2009), which is seen in the peak wet phase of the second event (Fig. 8b), the observed anomalous circulation linked to the biweekly IO convection may be a mixture of the two wave trains, EAP and IO–TP–EA. This is verified by the results of linear baroclinic model experiments forced with prescribed heating over the IO (Figs. S5–S6 in the ESM). In addition, the biweekly IO convection may have influenced the activity of the Eurasian mid-high latitudinal wave train through affecting the phase transition of the NAO (Liu et al., 2020).

      Figure 9.  The time–longitudinal cross section of (a) original OLR over the tropics averaged from 15ºS–15ºN and (b) its 10–20-day filtered component (shading, units: W m–2), together with the spectral analysis of OLR (c) over the tropical Indian Ocean (15ºS–15ºN, 75º–90ºE) and (d) over the tropical western Pacific (15ºS–15ºN, 110º–125ºE) during early summer 2020. (e) The phase space diagram of the MJO, with black, red, green, and blue indicating May, June, July, and August, individually.

    • On the seasonal time scale, the convection is anomalously active and generally anchored over the tropical western-central IO (Figs. 9a and 9e) (Zhou et al., 2021). It is the strongest on record since 1979 (Fig. S7 in the ESM). One MJO-based statistical calculation performed here suggests that the duration of the stationary convection persists for 59 days, ranking the first within the last 40 years (Fig. S8 in the ESM) and explaining a substantial fraction of daily rainfall in 2020 (Fig. S9 in the ESM). For the composite in the years when the western-central IO convection is stronger or weaker, the seasonal convection has a substantial impact on seasonal mean circulation and rainfall (Figs. S10–S12 in the ESM). This is in agreement with Zhang et al. (2021) and Wang et al. (2022), which illustrate the substantial role of the abnormally long-lasting, stagnant active MJO phase in the IO.

      Figure 10.  Composites of early summer OLR anomalies (shading, units: W m–2) based on the years with (a) enhanced and (b) weakened biweekly precipitation. Blue boxes indicate the area of the western Indian Ocean (40º–80ºE, 20ºS–20ºN) with significant convection anomalies.

      The role of the seasonal mean IO convection in modulating the biweekly rainfall component in the MLYR can be seen from the observational composite. Using 0.5 standard deviations as the threshold, a total of 10 (13) cases with strong (weak) biweekly precipitation are derived from 1981 to 2020. The composite OLR anomalies in these two types of cases are shown in Fig. 10. Corresponding to the strong (weak) biweekly precipitation, the composite seasonal IO convection is significantly intensified (weakened) (Fig. 10). Such a correspondence is also seen from their substantial correlation (–0.56) (Fig. 11). Thus, the strongest biweekly precipitation on record over the MLYR, occurring in 2020, is closely connected with the strongest seasonal convection on record in the western-central IO.

      Figure 11.  Scatterplots of the Indian Ocean OLR anomaly index with (a) the quasi-biweekly precipitation index over the MLYR (QBW-rain), (b) the quasi-biweekly EAP intensity index (QBW_EAP), and (c) the quasi-biweekly Z500 index near Lake Baikal [QBW-Z500 (bkl)]. The blue (red) dots represent the years with stronger (weaker) quasi-biweekly precipitation, while the asterisk indicates year 2020.

      To further highlight the modulating role of the seasonal convection, we compare the activity of intraseasonal wave trains under two opposite IO convection types (strong versus weak). According to the seasonal mean OLR anomaly over the western-central IO (40°–80°E, 20°S–20°N), a total of 13 years with stronger convection and 16 years with weaker convection are identified (Fig. S7). We define the EAP index as IEAP = ${-0.25Z}_{\mathrm{s}}'(60^\circ \mathrm{N},\mathrm{ }120^\circ \mathrm{E})\,+\,{0.5Z}_{\mathrm{s}}'(40^\circ \mathrm{N},\mathrm{ }120^\circ \mathrm{E}){\, -\, 0.25Z}_{\mathrm{s}}' (20^\circ \mathrm{N}, \mathrm{ }120^\circ \mathrm{E})$ and the geopotential height anomaly index near Lake Baikal as IBkl =${\mathrm{Z}'}_{\mathrm{s}} (60^\circ \mathrm{N},\mathrm{ }120^\circ \mathrm{E})$ (Huang, 2004). A biweekly filter is applied to the geopotential heights before calculating the two indices.

      Figure 11 displays the scatterplots of the seasonal IO convection index, the biweekly precipitation over the MLYR, IEAP, and IBkl. The EAP-like wave train and the geopotential heights near Lake Baikal are negatively correlated with the IO OLR, with coefficients of –0.46 (99%) and –0.33 (95%), respectively. Additionally, the biweekly components of the EAP-like pattern and the geopotential heights near Lake Baikal in 2020 are the strongest and second strongest on record, respectively (Figs. 11b, c). This means that when IO convection is active, the biweekly fluctuation of the EAP and the geopotential height anomalies near Lake Baikal are strong. This is in agreement with the modulation of IO convection on the biweekly periodicity of the extratropical Eurasian wave train (Fig. S13 in the ESM).

      The modulating role of seasonal IO convection can also be seen from the comparison in variance of biweekly Z500 components between when the convection is stronger and when it is weaker (Fig. S14 in the ESM). During the summer, with stronger IO convection, the biweekly variances over the mid-high latitudinal western North Pacific and the northeast region of Lake Baikal are increased (Fig. S14a). Additionally, there is enhanced westerly wind over mid-latitudinal central Asia but weakened and northward-shifted westerly wind in the western North Pacific (Fig. S12a). The former favors a southward shift of the Asian jet exit, which is conducive to the southeastward propagation of the Eurasian mid-high latitudinal wave train. The latter favors the northward shift of the North Pacific wave train and its westward propagation as well (Fig. 7), which will be analyzed in the next section. The intensified biweekly variances over Siberia and the North Pacific are in agreement with this change in background westerly winds (Figs. S12c, S14c in the ESM).

      The above analyses have revealed the important role of the IO convection in shaping the strongest biweekly rainfall on record in two ways. First, the IO convection exhibits a strong biweekly variability, which modulates the biweekly activities of the meridional PJ/EAP wave train and mid-high latitudinal Eurasian wave train. Second, the IO convection in June–July 2020 is also the strongest of the season and is persistent. This unusual seasonal background also modulates the intensity and propagation of the biweekly wave train activities through changing the atmospheric background flow.

    5.   Mechanism for the maintenance and propagation of the extratropical wave trains
    • The development and maintenance of the mid-high latitudinal wave train often depend on kinetic energy extraction from the basic state (Simmons et al., 1983; Ding and Wang, 2007; Wang et al., 2013). The biweekly kinetic energy conversion (CKiso) is thus calculated (Fig. 12). The maximum positive centers of CKiso are located in the Atlantic near 40ºW and the North Pacific around 45ºN. These CKiso anomalies (Fig. 12c) correspond well to the positions of the Eurasian and North Pacific wave trains (Fig. 7g), indicating that the seasonal basic flow is favorable for converting kinetic energy to the biweekly perturbation. Since the seasonal basic flow is closely related to the tropical thermal forcing, this explains the finding in subsection 4.2 that the biweekly activity is modulated by the IO convection.

      Figure 12.  Horizontal distribution of barotropic energy conversion of quasi-biweekly fluctuations (10–5 m2 s–3) at 250 hPa for (a) summer 2020, (b) the climatological mean, and (c) the difference of (a) minus (b).

    • Recently, Du and Lu (2021) identified biweekly North Pacific wave trains during summer, which propagate zonally eastward along the upper westerly jet and are non-phase-locking. Their development and maintenance depend on energy absorbance from the basic flow through baroclinic energy conversion. Why the biweekly North Pacific wave train in 2020 shifts to a northern location and propagates westward rather eastward (cp. Du and Lu, 2021) is intriguing.

      The propagation of a Rossby wave obeys the phase speed formula (Holton, 2004):

      Thus, a weak basic flow ($\bar{u}$) and a small wavenumber may cause the westward propagation of a wave train. As seen from Fig. 13, the 200-hPa background westerly flow over the North Pacific in summer 2020 is significantly weakened and shifts northward. Also, the wavenumber is evidently smaller than that identified by Du and Lu (2021) (cp. Fig. 7 with their Fig. 4). These anomalies may facilitate the westward propagation and northward shift of the North Pacific wave train. Additionally, the location of the 200-hPa background westerly core over the western North Pacific matches well with the location of maximum CKiso (cp. Figs. 12a, 13a), consistent with the fact that the biweekly perturbations obtain kinetic energy from the basic flow.

      Figure 13.  Horizontal distribution of U200 (m s–1) for (a) summer 2020, (b) the climatological mean, and (c) the difference of (a) minus (b).

      Figure 14.  Schematic illustrating that the biweekly wave trains converge over the MLYR to induce the strongest recorded rainfall in June–July 2020. The four black arrowed lines indicate the quasi-biweekly wave trains. In the upper panel, the red (black) contour indicates the climatological (observed in 2020) 5880-gpm contour of Z500, and shading indicates Z500 anomalies. In the lower panel, the red (blue) arrow represents anomalous warm and wet (cold and dry) flows originating from the South China Sea and Bay of Bengal (the north), and shading represents seasonal OLR anomalies.

      The upper-level westerly winds are closely related to the meridional thermal contrast (Coumou et al., 2015; Dong et al., 2022). An intensified meridional thermal contrast occurs in a northern location of the western North Pacific (Figs. S15c and S16c). Furthermore, the abnormal meridional thermal contrast may be related to the sea surface temperature (SST) with abnormal warmth in the Kuroshio extension (Fig. S17c). The warmer SST in the Kuroshio extension favors weakening and northward shifting of the North Pacific jet (Ogawa et al., 2012). Thus, the anomalous biweekly activity of the North Pacific wave train may be further attributed to the beneath-boundary SST forcing.

    6.   Summary and discussion
    • During June–July 2020, a record-setting mei-yu event occurred over the MLYR. In this study, we analyzed the intraseasonal evolution of the mei-yu rainfall and the associated atmospheric circulation in 2020 and explored the underlying influential factors. Our results are summarized into a schematic diagram (Fig. 14) and can be described as follows.

      (1) The biweekly component of the total precipitation in 2020 ranks the strongest within the past 40 years. This strongest biweekly rainfall component together with the strongest recorded synoptic component collaboratively shapes the record-breaking mei-yu event in 2020;

      (2) The biweekly rainfall component is induced jointly by the biweekly variations of the WPSH and the mid-tropospheric geopotential heights near Lake Baikal, and both these factors are also nearly the strongest in the past 40 years. The biweekly activity of the WPSH is attributed to the fluctuation of the PJ/EAP wave train; the biweekly anomalies of the geopotential height near Lake Baikal are associated with the two intersecting wave trains over mid-high latitudinal Eurasia and the North Pacific; it is the intensified or weakened convergence over the MLYR of the warm-wet flows from the south with the dry-cold air from the north that causes the biweekly fluctuation of the mei-yu rainfall;

      (3) The strongest recorded IO convection occurred in 2020 and may have been partly responsible for the biweekly southward retreat (northward stretch) of the WPSH via modulation of the PJ/EAP; it may also have been partly responsible for the biweekly fluctuations of the geopotential heights near Lake Baikal through modulation of the extratropical wave train activities. The biweekly IO convection exhibits a seesaw variation with the convection over the Western Pacific. The latter triggers the anomalous biweekly activity of the EAP wave train. In comparison, the strongest seasonal IO convection component intensifies the thermal contrast between the tropics and the mid-high latitudes, changes the atmospheric background flow. and in turn modulates the extratropical wave trains;

      (4) The barotropic kinetic energy conversion from the mean flow to the biweekly perturbation confirms that the background flow in 2020 favors the occurrence of two midlatitude wave trains. Additionally, the westward propagation of the North Pacific wave train is attributed to the weakened and northward-shifted upper-level westerly winds over the North Pacific, which may be caused by the anomalous meridional thermodynamic gradient due to the warm SST anomalies in the Kuroshio extension in 2020.

      Here, we found that both the biweekly and synoptic rainfall components of the 2020 mei-yu event were the strongest on record, and we revealed the collaborative role of the biweekly wave train activities from the tropics and the mid-high latitudes. The primary paths include: the tropical one, through modulating the PJ/EAP teleconnection to affect the WPSH, and the mid-high latitudinal one, through modulating the Eurasian and North Pacific wave trains to affect the mid-tropospheric geopotential heights near Lake Baikal and cold air activity. Then, the anomalous biweekly activities of these wave trains are related to the strongest recorded convection over the tropical IO. Additionally, the occurrence of the two midlatitude wave trains is associated with the energy conversion from the mean flow to the biweekly perturbation, and the westward retreat of the North Pacific wave train is linked to the weakened and northward-shift upper-level westerly winds linked to anomalous SST. These results have not been revealed well in previous studies, and thus are innovative to some extent.

      There are caveats in this study. First, only the intraseasonal (biweekly) rainfall component, rather than the synoptic component, was analyzed here. In fact, the synoptic rainfall component is also the strongest on record and has explained the largest fraction of the mei-yu rainfall (Fig. S3). Second, the modulating role of seasonal IO convection on the biweekly wave train activities is derived only through observational composite analysis. Analyses through numerical experiments are needed in the future.

      There are also several unsolved questions. The first is why the IO convection is so unique in early summer 2020. Previous studies have pointed to the low-level anomalous easterlies or the relatively stronger easterly shear over the equatorial Indian Ocean–Pacific (Yuan et al., 2014; Lu and Hsu, 2017; Zhang et al., 2019a), because such wind anomalies may hinder the eastward propagation of the MJO (Fig. S10a). Recently, Takaya et al. (2020) suggested the role of the warm IO condition, which can be traced back to the super Indian Ocean Dipole (IOD) in autumn 2019. This provides some valuable insights into future longer-lead forecasting on biweekly rainfall over the MLYR. The second unsolved question is what causes the difference in the dominant period of intraseasonal mei-yu rainfall. The cycle in the 2020 case is somewhat similar to 1991 (Mao and Wu, 2006), but different from 1998 and 2016. The latter two cases have a significant longer period of 30–60 days (Zhu et al., 2003; Shao et al., 2018). Third, whether other oceanic forcing is involved is unknown. The 2016 and 1998 cases correspond to two extremely strong El Niño events in the preceding winter (Wu et al., 2018), but there is only a modest central-Pacific El Niño for the 2020 case. One recent study emphasizes the collaborative role of thermodynamic forcing in different oceanic basins, particularly from the North Atlantic SST anomaly (Zheng and Wang, 2021). This issue deserves investigation by conducting model sensitivity experiments. Also, coincidental with strong seasonal convections over the IO, there are two evident quasi-stationary tripole structures over Eurasia (Fig. S11), one located at high latitudes and extending zonally, while the other extends meridionally from the tropics to extratropics and resembles the EAP. These two structures also exist in the composite in the dry/wet phase (cp Figs. 6 and Fig. S11a). They feature a significant biweekly period when the IO convection is anomalously strong (Fig. S13). Thus, there is a potential overlap of the biweekly wave train activities discussed here and the two stationary wave trains. Nonetheless, in-depth studies are needed for improved understanding and isolating the occurrence mechanisms for the record-breaking mei-yu rainfall in 2020.

      Finally, Wang et al. (2012) suggested that the local air–sea interaction may play a role in atmospheric intraseasonal oscillations over the Kuroshio Extension region. Their analysis revealed that the intraseasonal SST variability over the midlatitude northern Pacific was caused by local atmospheric forcing through the change of intraseasonal shortwave radiation and latent heat flux anomalies. The SST anomalies, on the other hand, could help the transition of the atmospheric ISO through changing the atmospheric convective instability and inducing convective heating, and the related asymmetry could further modulate the propagation of the wave train. This mechanism may also have worked in 2020.

      Acknowledgements. This work is jointly supported by the National Key Research and Development Program of China (Grant No. 2018YFA0606403) and the National Natural Science Foundation of China (Grant Nos. 41731177 and 41790473).

      Electronic supplementary material: Supplementary material is available in the online version of this article at https://doi.org/10.1007/s00376-022-2050-1.

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