Interannual Variations in Summer Extreme Precipitation Frequency over Northern Asia and Related Atmospheric Circulation Patterns

Haixu Hong aNansen-Zhu International Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
cUniversity of Chinese Academy of Sciences, Beijing, China

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Jianqi Sun aNansen-Zhu International Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
bCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
cUniversity of Chinese Academy of Sciences, Beijing, China

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Huijun Wang bCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
aNansen-Zhu International Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Abstract

This study investigates the spatial–temporal variations in summer extreme precipitation event (EPE) frequency over northern Asia and related atmospheric circulations. The division analysis indicates that three subregions of western Siberia (WS), eastern Siberia (ES), and eastern Mongolia–northeastern China can be identified, and the EPE variations over WS and ES are focused on here. On an interannual time scale, higher EPE frequencies are related to a similar dipole pattern in the upper troposphere [anomalous cyclone (anticyclone) to the west (southeast) of these two subregions] and a local anomalous cyclone in the lower troposphere. The dipole pattern leads to anomalous air divergence in the upper troposphere and compensating ascending motion over the subregions; the local anomalous cyclone in the lower troposphere leads to water vapor convergence. These anomalous atmospheric circulations therefore provide favorable dynamic and moisture conditions for higher EPE frequencies. Further analysis indicates that the WS EPE frequency is influenced by the combination of polar–Eurasian (POL) and North Atlantic Oscillation (NAO) patterns, while the ES EPE frequency is influenced by Scandinavian (SCAND) [British–Baikal Corridor (BBC)] pattern over 1987–2004 (2005–15). The alternate influence on the ES EPE frequency may result from the interdecadal change in the structure of SCAND and BBC patterns. In addition, the East Asian summer monsoon (EASM) shows enhanced influence on ES EPE frequency after the late 1990s, which could be due to interdecadal strengthening and extending of the anomalous cyclone around Lake Baikal. This cyclone is concurrent with EASM, and its changes favor water vapor transported by EASM to ES after the late 1990s.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jianqi Sun, sunjq@mail.iap.ac.cn

Abstract

This study investigates the spatial–temporal variations in summer extreme precipitation event (EPE) frequency over northern Asia and related atmospheric circulations. The division analysis indicates that three subregions of western Siberia (WS), eastern Siberia (ES), and eastern Mongolia–northeastern China can be identified, and the EPE variations over WS and ES are focused on here. On an interannual time scale, higher EPE frequencies are related to a similar dipole pattern in the upper troposphere [anomalous cyclone (anticyclone) to the west (southeast) of these two subregions] and a local anomalous cyclone in the lower troposphere. The dipole pattern leads to anomalous air divergence in the upper troposphere and compensating ascending motion over the subregions; the local anomalous cyclone in the lower troposphere leads to water vapor convergence. These anomalous atmospheric circulations therefore provide favorable dynamic and moisture conditions for higher EPE frequencies. Further analysis indicates that the WS EPE frequency is influenced by the combination of polar–Eurasian (POL) and North Atlantic Oscillation (NAO) patterns, while the ES EPE frequency is influenced by Scandinavian (SCAND) [British–Baikal Corridor (BBC)] pattern over 1987–2004 (2005–15). The alternate influence on the ES EPE frequency may result from the interdecadal change in the structure of SCAND and BBC patterns. In addition, the East Asian summer monsoon (EASM) shows enhanced influence on ES EPE frequency after the late 1990s, which could be due to interdecadal strengthening and extending of the anomalous cyclone around Lake Baikal. This cyclone is concurrent with EASM, and its changes favor water vapor transported by EASM to ES after the late 1990s.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jianqi Sun, sunjq@mail.iap.ac.cn

1. Introduction

Under the background of global warming, the global average intensity and frequency of extreme precipitation events (EPEs) are rapidly increasing (e.g., Frich et al. 2002; Alexander et al. 2006; IPCC 2013; Lehmann et al. 2015; Donat et al. 2016; Sun et al. 2021). As one of the most sensitive regions to global warming, northern Asia (NA) has suffered from intense EPEs, especially during the twenty-first century. For instance, in 2013, heavy rain occurred in the Amur River (called the Heilong River in China) basin, causing the worst floods in 140 towns over the past 120 years in Russia (WMO 2014) and affecting more than 800 000 people with at least 200 deaths or missing people in China (Danilov-Danilyan et al. 2014). In 2019, a severe EPE hit the Irkutsk region (located in southeastern Siberia) and caused the largest record-breaking flood over the past 80 years on the Iya River, taking 24 lives and affecting more than 33 000 people (Vilfand et al. 2020). Furthermore, some studies have shown that the increasing and strengthening trend of NA EPEs will continue into the future (e.g., Sillmann et al. 2013; IPCC 2013; Aleshina et al. 2019; Ao et al. 2020). Due to the catastrophic consequences of EPEs, it is necessary to investigate the variations in EPEs over NA and to explore their possible mechanisms.

Previous studies have explored some variation features of EPEs in NA. Because summer is the rainy season in this region, summer EPEs in NA are the main focus of these studies. Along with the warming temperature, precipitation with higher intensity occurs more frequently over northern Eurasia (Ye et al. 2016; Chernokulsky et al. 2019), indicating that the NA summer precipitation is becoming more extreme. For the Russian part of NA, the stations with significant increasing trend in EPE intensity are more than the global average level (Contractor et al. 2021; Sun et al. 2021), but the EPE frequency shows an insignificant increasing trend (Bulygina et al. 2007; Groisman et al. 2013; Degefie et al. 2014; Aleshina et al. 2019). For Mongolia, summer precipitation shows no significant trend (Batima et al. 2005), but EPEs are increasing over most areas except northern Mongolia (Endo et al. 2006). For northeastern China (NEC), the summer EPE frequency shows a weak trend but has interdecadal variation (Xu et al. 2011; Du et al. 2013; Wang et al. 2013; Song et al. 2015; Wang et al. 2017; Yang et al. 2017; Cao et al. 2018). Although these previous studies have revealed some features of EPE variation over NA, the dominant modes of NA EPEs on an interannual time scale are still unclear, which is one research topic in this study.

Concerning the mechanisms, most previous studies have mainly focused on summer-mean precipitation variation over NA. On interannual time scale, the East Asian summer monsoon (EASM) and west Pacific subtropical high (WPSH) significantly influence the NEC summer-mean precipitation (e.g., Han et al. 2015; Wang et al. 2017; Sun et al. 2017; Han et al. 2019; Tang et al. 2021). In addition, the NA summer-mean precipitation is closely related to atmospheric teleconnection patterns, such as the summer North Atlantic Oscillation (NAO; Sun and Wang 2012), Silk Road pattern (He et al. 2018), Eurasian zonal Rossby wave train (Fukutomi et al. 2003; Iwao and Takahashi 2006, 2008; Fujinami et al. 2016), east Atlantic pattern and east Atlantic/western Russian pattern (Ye et al. 2016), and East Asia–Pacific and Eurasian patterns (Hu et al. 2020). On interdecadal time scale, the Pacific decadal oscillation has been shown to contribute to the interdecadal decrease in NEC summer precipitation in the late 1990s (Han et al. 2015), and Atlantic multidecadal variability is associated with the interdecadal variation in warm season precipitation in Siberia (Sun et al. 2015).

Compared with summer-mean precipitation, the mechanisms of NA EPE variation have received much less attention. Few related studies have examined atmospheric patterns associated with NEC EPE variation. For example, Cao et al. (2018) and Chen et al. (2019) revealed that NEC EPE variation is related to a dipole circulation pattern over the region. The activity of cutoff lows may contribute to severe summer EPEs over NEC (Zhao and Sun 2007; Hu et al. 2010; Cao et al. 2018; Tang et al. 2021). To date, the mechanisms related to NA EPE variation still lack systematic understanding. Therefore, in this study, we first investigate the dominant modes of summer NA EPE interannual variations and then explore the possible mechanisms for the variations, with the aim of deepening our understanding of NA EPE variation and further providing information for NA EPE prediction.

The paper is organized as follows: section 2 depicts the data and methods, section 3 explores the dominant spatial–temporal variability features of the NA EPE frequency, section 4 investigates the atmospheric circulations responsible for the interannual variation in the NA EPE frequency, and section 5 presents the summary and discussion.

2. Data and methods

a. Datasets

To investigate the interannual variation in the summer NA EPE frequency, daily precipitation data from the Climate Prediction Center (CPC) Global Unified Gauge-Based Analysis of Daily Precipitation (CPC-global; Xie et al. 2010) are applied in this study. The dataset has combined all information sources available at the CPC and has improved the quality. CPC-global has a horizontal resolution of 0.5° × 0.5° and covers the period from 1979 to the present. In addition, daily precipitation data from the Global Precipitation Climatology Centre (GPCC; Ziese et al. 2020) version 2020 and Rainfall Estimates on a Gridded Network (REGEN; Contractor et al. 2020) version 2019 have also been employed to support the phenomenon observed in the CPC-global dataset. The GPCC and REGEN have the same horizontal resolution of 1° × 1°, and GPCC (REGEN) covers the period of 1982–2019 (1950–2016). Because of the great difference in NA regional average precipitation between CPC-global and REGEN over the period of 1979–81, we concentrate on the NA EPE frequency variation over the period of 1982–2020. The regional-mean summer rainfall over NA in CPC-global is significantly correlated to that in GPCC (REGEN) over the period of 1982–2019 (1982–2016), with a coefficient of 0.72 (0.61), reflecting that the NA precipitation shows consistent interannual variation in these precipitation datasets.

For atmospheric circulations, the monthly dataset of the Japanese 55-year Reanalysis (JRA-55; Kobayashi et al. 2015) is applied, which has a horizonal resolution of 1.25° × 1.25° and 37 pressure levels from 1000 to 1 hPa. The variables involved in this study include geopotential height, horizontal wind velocity, vertical velocity, relative divergence, and vertically integrated water vapor flux (from 1000 to 100 hPa).

In this study, both the NAO (Wallace and Gutzler 1981; Barnston and Livezey 1987) index and Scandinavian pattern (SCAND; Barnston and Livezey 1987) index are downloaded from the CPC (https://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml). The NAO index and SCAND index are defined as the standardized time series of the NAO pattern and SCAND pattern, respectively, which are obtained by the rotated principal component analysis results of the monthly mean 500-hPa geopotential height over the Northern Hemisphere (20°–90°N).

b. Definition and methods

NA is defined as the region over 40°–78°N, 60°–140°E, containing the Asian parts of Russia, Mongolia, northern China, eastern Kazakhstan, Kyrgyzstan, central Uzbekistan, and northern Tajikistan. Because the rainy season is summer in NA, the EPEs in summer [June–August (JJA)] are the focus of this study. Following previous studies (e.g., Karl et al. 1999; Frich et al. 2002; Alexander et al. 2006), EPEs are defined based on the relative threshold. For each grid, the 95th percentile of daily precipitation on rainy days (precipitation exceeding 1 mm) during JJA over the 30 years (1982–2011) is defined as the extreme precipitation threshold. A specific summer (JJA) day over the period of 1982–2020 with daily precipitation higher than the extreme precipitation threshold is defined as an EPE. The cumulative EPE number in a certain summer is considered as the EPE frequency of the summer.

To obtain the interannual (interdecadal) variation, the 9-yr high-pass (low-pass) Lanczos filter (Duchon 1979) is applied to the time series of the NA EPE frequency and atmospheric circulations. According to the advice from Duchon (1979), the 11-point moving time window is used in the 9-yr high-pass/low-pass filter for a better filtering effect and less data loss. Furthermore, the rotated empirical orthogonal function (REOF; Lorenz 1956; Richman 1986) using the raw varimax orthogonal rotation method (Kaiser 1958; Wilks 2011) is performed to explore the spatial distribution features of the NA EPE frequency.

The wave activity flux (Takaya and Nakamura 2001) is applied in this study to show the direction of wave energy propagation, and the horizontal component of the wave activity flux can be expressed as
W=p2a2|U|{u¯cosφ[(ψλ)2ψ2ψλ2]+υ¯(ψλψφψ2ψλφ)u¯(ψλψφψ2ψλφ)+υ¯cosφ[(ψφ)2ψ2ψφ2]}
where the overbar represents the average summer climate state and the prime represents the deviation from the climate state; p is the normalized pressure, which is calculated by pressure/1000 hPa; U=(u¯,υ¯) denotes the average horizontal wind velocity; ψ′ represents the perturbation streamfunction; λ and φ are longitude and latitude, respectively; and a is the radius of Earth.

3. Dominant modes of spatial–temporal variations in NA EPE frequency

The precipitation threshold of EPE, climatological precipitation amount and frequency of EPE in NA over the period of 1982–2020 are shown in Fig. 1. The precipitation threshold of EPE shows a highest (lowest) center in southeastern NA (central Asia) with values greater than 25 mm day−1 (less than 10 mm day−1) (Fig. 1a). The EPE average precipitation amount has a similar spatial distribution with EPE threshold but shows larger values (Fig. 1b). The climatological frequency of EPE exhibits a larger value zone extending from northwestern NA to southeastern NA, and the lower values locate on both sides of the zone (Fig. 1c).

Fig. 1.
Fig. 1.

The maps of (a) precipitation threshold of EPE (mm day−1), (b) climatological precipitation amount of EPE (mm day−1), and (c) climatological EPE frequency in NA over the period of 1982–2020.

Citation: Journal of Hydrometeorology 23, 5; 10.1175/JHM-D-21-0177.1

a. Regionalization based on the REOF results

In this study, the regionalization of NA is based on the REOF analysis. According to the REOF analysis, empirical orthogonal function (EOF) analysis is first applied to the standardized NA EPE frequency over 1982–2020, and then the first three EOF modes are retained and further subjected to varimax orthogonal rotation to achieve the leading REOF modes. As shown in Fig. 2, the larger values are over eastern Siberia in the first REOF mode (REOF1) (Fig. 2a; north of 55°N and east of 110°E), western Siberia in the second REOF mode (REOF2) (Fig. 2b; north of 55°N and west of 110°E), and eastern Mongolia–northeastern China in the third REOF mode (REOF3) (Fig. 2c; south of 55°N and east of 110°E). Therefore, NA is divided into these three subregions. Different from these three subregions, southwestern NA cannot be significantly separated from other regions using the REOF method, which could be due to the much lower precipitation and EPE frequency there. Our statistical analysis indicates that the summer climatological precipitation over southwestern NA is less than 0.5 mm day−1 and that the rain days (daily precipitation exceeding 1.0 mm) are less than 20 days over the period of 1982–2011 (figure not shown); moreover, the summer average EPE frequency in southwestern NA is less than 1 day, which is significantly less than that in other subregions.

Fig. 2.
Fig. 2.

The (a) REOF1, (b) REOF2, and (c) REOF3 patterns of NA EPE frequency and the regression maps of EPE frequency against the (d) ES_avg index, (e) WS_avg index, and (f) EMNEC_avg index over the period of 1982–2020. The black dashed lines in the figures divide NA into three subregions. The explained variance of each mode to the total variance is shown in the top-right corner of (a)–(c). Dotted areas in (d)–(f) are significant at the 90% confidence level based on Student’s t test.

Citation: Journal of Hydrometeorology 23, 5; 10.1175/JHM-D-21-0177.1

To further examine the reasonability of such a subregion division, we define the ES_avg index, WS_avg index, and EMNEC_avg index as the standardized time series of regional mean EPE frequency over eastern Siberia (ES), western Siberia (WS), and eastern Mongolia–northeastern China (EMNEC). As shown in Figs. 2d–f, the EPE frequencies associated with the ES_avg, WS_avg, and EMNEC_avg indices, show consistent variations over their corresponding subregions. These results further confirm that such a subregion division is reasonable, and it is necessary to perform a regionalization in investigating the variation of NA EPE frequency.

b. Interannual variations in NA EPE frequency over subregions

As reviewed in section 1, previous studies have revealed some features of variations in EPE frequency over NEC and the associated mechanisms (e.g., Xu et al. 2011; Du et al. 2013; Wang et al. 2013; Song et al. 2015; Wang et al. 2017; Yang et al. 2017; Cao et al. 2018; Han et al. 2019; Chen et al. 2019). However, limited attention has been given to the interannual variations in EPE frequency over WS and ES. Therefore, in this study, the interannual variations in EPE frequency over these two subregions are emphasized.

The EPE frequencies over WS and ES show different variations on both decadal and interannual time scales. As shown in Figs. 3a and 3b, the WS_avg index has an interdecadal increase in the late 1990s, while the ES_avg index has an interdecadal increase in the early 2000s and a decreasing trend after the mid-2010s. Focusing on the interannual variation, we further define the ES_EPE index and WS_EPE index as the 9-yr high-pass-filtered and standardized time series of regional average EPE frequency over ES and WS, respectively. According to the 9-yr moving standard deviation results (figure not shown), the WS_EPE index shows enhanced variability after the mid-2000s, while the ES_EPE index exhibits comparable variability during the period (Figs. 3c,d). Moreover, as shown in Figs. 3e and 3f, the WS_EPE index- and ES_EPE index-related EPE frequencies on the interannual time scale have consistent variations over their own domains and the signals over other domains are weak.

Fig. 3.
Fig. 3.

Time series of (a) standardized (solid curve) and 9-yr low-pass-filtered WS_avg index (dashed curve) over the period of 1982–2020. (b) As in (a), but for the ES_avg index. The standardized time series of the (c) WS_EPE index and (d) ES_EPE index and regression maps of the 9-yr high-pass-filtered EPE frequency against the (e) WS_EPE index and (f) ES_EPE index over the period of 1987–2015. The red and blue shaded areas in (a) and (b) represent the positive and negative values of 9-yr low-pass-filtered index, respectively. The dotted areas shown in (e) and (f) are significant at the 90% confidence level based on Student’s t test. The blue dashed rectangles in (e) and (f) represent the scope of western Siberia and eastern Siberia, respectively.

Citation: Journal of Hydrometeorology 23, 5; 10.1175/JHM-D-21-0177.1

In addition, the correlation coefficient between the WS_EPE index and ES_EPE index is 0.33 and not significant at 95% confidence level. This analysis indicates the independent variability of the EPE frequency over WS and ES, and it is necessary to investigate their variabilities separately. In this study, the interannual variation in the EPE frequency over ES and WS and the related atmospheric circulations are mainly focused. Their decadal variation and mechanisms will be systematically investigated in future work.

4. Possible mechanisms for the interannual variations in NA EPE frequency over WS and ES

a. Local atmospheric circulations

The local atmospheric circulations associated with the interannual variations in the NA EPE frequency over WS and ES are first investigated in this subsection. As shown in Figs. 4a and 4b, corresponding to more EPE frequencies, the anomalous 200-hPa geopotential heights display a zonal dipole pattern, generally with anomalous cyclonic (anticyclonic) circulation to the west (southeast) of WS and ES. The anomalous strong winds between the cyclonic and anticyclonic circulations in the upper troposphere contribute to anomalous divergence over the two subregions (Figs. 4c,d).

Fig. 4.
Fig. 4.

Regression maps of 9-yr high-pass-filtered, JJA-mean (a),(b) 200-hPa geopotential heights (shading; gpm) and horizontal wind (vectors; m s−1), (c),(d) 200-hPa divergence (shading; 10−7 s−1), (e),(f) 850-hPa geopotential heights (shading; gpm) and horizontal wind (vectors; m s−1), (g),(h) vertically integrated water vapor flux (vectors; kg m s−1) and its divergence (shading; 10−6 kg s−1), and (i),(j) 500-hPa vertical velocity (10−3 Pa s−1) against the (left) WS_EPE index and (right) ES_EPE index over the period of 1987–2015. The dotted areas are significant at the 90% confidence level based on Student’s t test. Western Siberia (left column) and eastern Siberia (right column) are outlined by blue dashed rectangles.

Citation: Journal of Hydrometeorology 23, 5; 10.1175/JHM-D-21-0177.1

At 850 hPa, the two subregions of WS and ES are covered by anomalous cyclonic circulations (Figs. 4e,f). These anomalous cyclonic circulations in the lower troposphere favor local anomalous convergences of water vapor (Figs. 4g,h). This configuration of upper-level divergence and low-troposphere convergence circulations demands a compensating upward vertical motion over the two subregions, according to the law of mass conservation. Consequently, there are significant local ascending motions over the two subregions, corresponding to higher EPE frequencies there (Figs. 4i,j). Therefore, the local atmospheric circulations of the upper-tropospheric dipole pattern and lower-tropospheric cyclone provide favorable dynamic and moisture conditions for higher EPE frequencies over WS and ES.

In addition, Figs. 4g and 4i also show some significant signals of moisture and vertical motion over EMNEC, which are opposite to the signals over WS. These results indicate that the interannual variation in EPE frequency over WS has a reverse relationship with that over EMNEC. Sun and Wang (2012) have indicated that NAO is correlated to the reverse variation in summer-average precipitation over central East Asia and WS, and the correlation is enhanced after the late 1970s. In this study, we find that similar to summer-average precipitation, the EPE frequency also has reverse interannual variation over WS and EMNEC.

b. Combined influence of polar–Eurasian teleconnection and NAO pattern on the interannual variation in WS EPE frequency

In the last subsection, the local atmospheric circulations associated with the EPE frequency over WS and ES are studied. In this subsection, atmospheric teleconnections are investigated to further understand the mechanisms for the variations in EPE frequency and its related local atmospheric circulations.

The WS EPE-related 700-hPa geopotential heights are shown in Fig. 5a (figures at 200- and 500-hPa geopotential heights are similar and not shown). The figure reflects a quasi-stationary Rossby wave train propagating from the North Atlantic to NA and resulting in an anomalous cyclone–anticyclone dipole pattern over NA. The anomalous cyclonic circulation over WS is an important local atmospheric circulation in the lower troposphere influencing the interannual variation in the WS EPE frequency.

Fig. 5.
Fig. 5.

Regression maps of 9-yr high-pass-filtered, JJA-mean 700-hPa geopotential heights (shading; gpm) against (a) WS_EPE index and (b) POL index and the related horizontal wave activity flux (vectors; m2 s−2) over the period of 1987–2015. (c) The standardized time series of the WS_EPE index and POL index over the period of 1987–2015. The dashed boxes in (a) indicate the key regions for the definition of the POL index. The dotted areas are significant at the 90% confidence level based on Student’s t test. “A” and “C” indicate anomalous anticyclonic and cyclonic circulations, respectively. The correlation coefficient between the POL index and WS_EPE index is shown in (c).

Citation: Journal of Hydrometeorology 23, 5; 10.1175/JHM-D-21-0177.1

The pronounced Rossby wave train shown in Fig. 5a resembles the polar–Eurasian teleconnection pattern (POL), which is one of the atmospheric internal modes found by Barnston and Livezey (1987) at 700-hPa geopotential heights. Previous studies have explored the influence of the POL pattern on Eurasian climate (e.g., Yin et al. 2014; Gao et al. 2017; Li et al. 2018; H. Li et al. 2020). In this study, the POL index is defined based on Fig. 5a:
POL=12[Z700(45°60°N,100°130°E)Z700(60°–75°N,60°110°E)],
where Z700 represents the 9-yr high-pass-filtered JJA-mean 700-hPa geopotential heights. As shown in Fig. 5b, the POL-related wave train pattern is similar to that of EPEs over WS. The WS_EPE and POL indices show a consistent variation over the period of 1987–2015 (Fig. 5c), with a correlation coefficient of 0.60 (significant at the 99% confidence level). The results indicate that the POL pattern may be related to the interannual variations in EPE frequency over WS.

To further investigate the influence of the POL pattern on the EPE frequency over WS, the POL-related 200-hPa divergence and 500-hPa vertical velocity are shown in Figs. 6a and 6b. The figure displays alternate anomalous divergences and convergences along the propagating route of the POL pattern (Fig. 6a), which are correspondingly associated with alternate anomalous ascending and sinking motions over northern Eurasia (Fig. 6b). The results show that the POL pattern favors 200-hPa divergence and 500-hPa ascending motion over WS, providing favorable dynamic conditions for higher EPE frequencies over WS.

Fig. 6.
Fig. 6.

Regression maps of the 9-yr high-pass-filtered, JJA-mean (a) 200-hPa divergence (10−7 s−1), (b) 500-hPa vertical velocity (10−3 Pa s−1), and (c) vertically integrated water vapor flux (vectors; kg m s−1) and its divergence (shading; 10−6 kg s−1) against the POL index over the period of 1987–2015. The dotted areas are significant at the 90% confidence level based on Student’s t test. Western Siberia is outlined by blue dashed rectangles. “A” and “C” indicate anomalous anticyclonic and cyclonic circulations, respectively.

Citation: Journal of Hydrometeorology 23, 5; 10.1175/JHM-D-21-0177.1

In addition to the dynamic conditions, the POL pattern also influences the moisture condition over WS. Over northern Eurasia, westerly water vapor transport dominates the moisture condition. As shown in Fig. 6c, the POL-related anomalous northwesterly between the anticyclone over western Russia and the cyclone over WS in the lower-troposphere transport water vapor from the Arctic Ocean and high-latitude North Atlantic Ocean to WS. The water vapor transport route related to the POL pattern from the Arctic Ocean to WS is consistent with that related to WS EPEs (Fig. 4g). In addition, the POL pattern can enhance water vapor convergence over WS. Consequently, the POL pattern provides favorable moisture conditions for more EPEs over WS. Therefore, the POL pattern can influence the interannual variation in EPE frequency over WS by modulating the local atmospheric circulation and providing dynamic and water vapor conditions for EPEs.

On the other hand, there are also some differences between the atmospheric circulations associated with the POL pattern and WE EPEs. Comparing Figs. 5a and 5b, we can see that the WS EPE-related wave train pattern has a more significant anomalous anticyclone centered over Greenland. This anomalous anticyclone, combined with the cyclones over the midlatitude North Atlantic and western Europe, composes a NAO-like pattern. The NAO pattern is a dominant atmospheric mode (e.g., Wallace and Gutzler 1981; Barnston and Livezey 1987; Hurrell et al. 2003), and it has a great effect on summer rainfall over Europe and East Asia (e.g., Vicente-Serrano and López-Moreno 2008; Gu et al. 2009; Sun and Wang 2012). The pronounced difference indicates that the NAO pattern could also be another influential atmospheric factor of WS EPE frequency and that its influence could be independent of that of the POL. To confirm this deduction, the WS EPE-related atmospheric circulations after removing the POL signal are further analyzed. As shown in Fig. 7a, after removing the POL signal, the WS EPE-related 500-hPa (figures at 200 and 850 hPa are similar and not shown) geopotential heights display a more significant NAO pattern (shown in Fig. 7b). This result suggests that the NAO is related to a barotropic zonal wave train propagating from the mid-to-high latitudes of the North Atlantic to northeastern China. Although no significant signal is exhibited in the geopotential heights over WS, a strong anomalous cyclone locates in northern Europe (Fig. 7b). The anomalous cyclone-related anomalous westerly wind transports more water vapor from the midlatitudes of the North Atlantic to WS (Fig. 7b).

Fig. 7.
Fig. 7.

Regression maps of 9-yr high-pass-filtered, JJA-mean 500-hPa geopotential heights (shading; gpm) and vertically integrated water vapor flux (vectors; kg m s−1) against (a) WS_EPE index, from which the POL signal has been removed, and (b) opposite signed NAO index over the period of 1987–2015. (c) The standardized time series of WS_EPE and opposite signed NAO index over the period of 1987–2015. The correlation coefficient between the WS_EPE index and NAO index, and the correlation coefficient after removing the POL index are shown in (c). The dotted areas are significant at the 90% confidence level based on Student’s t test. Western Siberia is outlined by blue dashed rectangles. “A” and “C” indicate anomalous anticyclonic and cyclonic circulations, respectively.

Citation: Journal of Hydrometeorology 23, 5; 10.1175/JHM-D-21-0177.1

The WS_EPE index and NAO index are shown in Fig. 7c. The two indices vary consistently over the period of 1987–2015 and have a correlation coefficient of −0.50 (significant at the 99% confidence level). In addition, the NAO index has a weak relationship with the POL index, and their correlation coefficient is only −0.03. After removing the POL index, the correlation coefficient between the NAO index and WS_EPE index increases to −0.60 over the period of 1987–2015 (significant at the 99% confidence level), becoming stronger. These results indicate that the influence of the NAO pattern on EPE frequency over WS may be independent of the POL pattern.

The combined influence of these two patterns is further studied. First, the WS_EPE index is linearly fitted based on the POL and NAO indices, and the fitting result is defined as the WS_Fit index, which can be expressed as follows:
WS_Fit=0.58×POL0.48×NAO,
where POL and NAO represent the POL index and NAO index, respectively. The WS_Fit index–related 200-hPa divergence and 500-hPa vertical velocity reflect strong anomalous air divergence in the upper troposphere and ascending motion over WS (Figs. 8a,b), favoring a higher EPE frequency over WS. In addition, the combination of the NAO and POL patterns can enhance water vapor transportation from the midlatitude North Atlantic and Arctic Ocean to WS, and can result in strong water vapor convergence over the region (Fig. 8c), providing moisture conditions for a higher EPE frequency over WS. Furthermore, the WS_Fit index and WS_EPE index covary very well over the period of 1987–2015 (Fig. 8d), with a correlation coefficient of 0.77 (significant at the 99% confidence level), and the WS_Fit index can explain 59.3% of the total variation in the WS_EPE index. The results indicate that the interannual variation in the WS EPE frequency is dominated by the combined influence of the POL and NAO patterns.
Fig. 8.
Fig. 8.

Regression maps of 9-yr high-pass-filtered, JJA-mean (a) 200-hPa divergence (10−7 s−1), (b) 500-hPa vertical velocity (10−3 Pa s−1), and (c) vertically integrated water vapor flux (vectors; kg m s−1) and its divergence (shading; 10−6 kg s−1) against WS_Fit index over the period of 1987–2015. (d) The standardized time series of WS_EPE and WS_Fit index over the period of 1987–2015. The dotted areas are significant at the 90% confidence level based on Student’s t test. Western Siberia is outlined by blue dashed rectangles.

Citation: Journal of Hydrometeorology 23, 5; 10.1175/JHM-D-21-0177.1

c. Alternative influence of the Scandinavian teleconnection pattern and British–Baikal Corridor on EPE frequency in ES

The ES EPE-related 500-hPa geopotential heights exhibit two anomalous anticyclones over Scandinavia and EMNEC and an anomalous cyclone over central Siberia (Fig. 9a). Such a distribution of anticyclones and cyclones resembles the well-known SCAND pattern, which was explored by Barnston and Livezey (1987) as the Eurasia-1 pattern. Previous studies have indicated the influence of SCAND on the Northern Hemisphere climate (e.g., Bueh and Nakamura 2007; Lin 2014; Choi et al. 2020; Wang and Tan 2020). In this study, the connection between the SCAND pattern and ES EPE frequency is investigated. The standardized time series of the ES_EPE and SCAND indices are shown in Fig. 9b, which reflects that the ES_EPE and SCAND indices have consistent variations before the mid-2000s and weak out-phase variations after the mid-2000s. The correlation coefficient between the ES_EPE index and SCAND index is 0.72 before the mid-2000s (1987–2004) but −0.13 afterward (2005–15). Moreover, their 9-yr running correlation coefficient also shows an interdecadal decrease in the mid-2000s (Fig. 9b).

Fig. 9.
Fig. 9.

(a) Regression maps of 9-yr high-pass-filtered, JJA-mean 500-hPa geopotential heights (shading; gpm) and horizontal wind (vectors; m s−1) against the ES_EPE index over the period of 1987–2015. (b) The standardized time series of the ES_EPE index (red solid curve) and SCAND index (blue dashed curve) over the period of 1987–2015 and the 9-yr running correlation coefficient between these two indices. (c),(d) As in (a), but against the ES_EPE index and SCAND index, respectively, over the period of 1987–2004. The 9-yr correlation coefficient in year i represents the correlation coefficient over the period from i − 4 to i + 4. The dotted areas are significant at the 90% confidence level based on Student’s t test. The brown line in (b) represents the threshold of the 90% confidence level for the 9-yr correlation coefficient. The correlation coefficients between ES_EPE and the SCAND index over the periods of 1987–2004 and 2005–15 are shown in (b). Eastern Siberia is outlined by blue dashed rectangles in (a), (c), and (d). “A” and “C” indicate anomalous anticyclonic and cyclonic circulations, respectively.

Citation: Journal of Hydrometeorology 23, 5; 10.1175/JHM-D-21-0177.1

The above index analysis indicates that the atmospheric patterns responsible for the interannual variation in the ES EPE frequency could have an interdecadal change in the mid-2000s. Here, the period of 1987–2004 is first emphasized. The ES EPE-related wave train pattern in Fig. 9c resembles the SCAND pattern in Fig. 9d. The three centers of the SCAND pattern in the ES EPE-related wave train over the Scandinavian Peninsula, central Siberia, and NEC are much stronger over period of 1987–2004 (Fig. 9c) than over period of 1987–2015 (Fig. 9a), further indicating the enhanced relationship between the SCAND pattern and ES EPE frequency over period of 1987–2004. The positive phase of the SCAND pattern can lead to upward motion to ES (Fig. 10b); in addition, it can enhance water vapor transportation to ES and can result in water vapor convergence over the region (Fig. 10d). The SCAND pattern-induced dynamic and moisture conditions are consistent with that of the ES EPE frequency in Figs. 10a and 10c. Therefore, the SCAND pattern is responsible for the interannual variation in EPE frequency over ES during the period before the mid-2000s.

Fig. 10.
Fig. 10.

Regression maps of 9-yr high-pass-filtered, JJA-mean (a) 500-hPa vertical velocity (10−3 Pa s−1) and (c) vertically integrated water vapor flux (vectors; kg m s−1) and its divergence (shading; 10−6 kg s−1) against the ES_EPE index over the period of 1987–2004. (b),(d) As in (a) and (c), but against the SCAND index. Eastern Siberia is outlined by blue dashed rectangles. “A” and “C” indicate anomalous anticyclonic and cyclonic circulations, respectively.

Citation: Journal of Hydrometeorology 23, 5; 10.1175/JHM-D-21-0177.1

The next question is why the relationship between the SCAND pattern and ES EPE frequency weakens after the mid-2000s. To answer this question, the SCAND pattern-related zonal circulations along 60°N over the periods of 1987–2004 and 2005–15 are examined (Fig. 11). The figure suggests that the SCAND pattern is related to significant anomalous sinking motion over 20°–60°E and anomalous ascending motions over 110°–140°E (ES) over the period of 1987–2004 (Fig. 11a). This zonal dipole pattern of vertical motions is associated with the anomalous anticyclone and cyclone of SCAND over northern Europe and central Siberia (Fig. 9d). However, over the period of 2005–15, the anomalous cyclone of the SCAND pattern over central Siberia shrinks westward (figure not shown), and the anomalous ascending motions over 110°–140°E also shift westward to 85°–100°E (Fig. 11b). The significant anomalous ascending motion over ES during 1987–2004 is replaced by insignificant vertical motion over 2005–15. These results indicate that the westward shift of the SCAND pattern weakens its relationship with the interannual variation in the ES EPE frequency over the period of 2005–15.

Fig. 11.
Fig. 11.

Latitude–pressure cross sections of regressed 9-yr high-pass-filtered vertical velocity (shading; 10−3 Pa s−1) and zonal circulations (vectors; zonal wind, m s−1 and vertical motion, 10−3 Pa s−1) along 60°N against the SCAND index over the periods of (a) 1987–2004 and (b) 2005–15. Eastern Siberia is outlined by blue lines.

Citation: Journal of Hydrometeorology 23, 5; 10.1175/JHM-D-21-0177.1

Different from the period of 1987–2004, the ES EPE-related 250-hPa geopotential heights (Fig. 12a) resemble the British–Baikal Corridor (BBC) teleconnection pattern (Fig. 12b), which propagates in the upper troposphere along the subpolar jet and influences the summer average rainfall and temperature over northern Eurasia (Xu et al. 2019). Following Xu et al. (2019), the BBC index is defined as the 9-yr high-pass-filtered and standardized principal component of the first leading mode of JJA-mean 250-hPa meridional wind over the region (50°–80°N, 20°W–150°E) during the period of 1982–2020. As shown in Fig. 12c, more consistent variations in the ES_EPE index and BBC index can be seen after the mid-2000s than before. The correlation between the ES_EPE index and the BBC index is only −0.11 over the period of 1987–2004 but increases to 0.70 (significant at the 98% confidence level) over the period of 2005–15. The interdecadal change in their relationship is further confirmed by their 9-yr running correlations (Fig. 12c), showing significant correlations after the mid-2000s. Therefore, the BBC pattern could have an enhanced influence on the ES EPE frequency after the mid-2000s.

Fig. 12.
Fig. 12.

As in Figs. 9c, 9d, and 9b, but (a),(b) against the ES_EPE index and BBC index over the period of 2005–15, respectively, and (c) for the ES_EPE index (red solid curve) and BBC index (blue dashed curve).

Citation: Journal of Hydrometeorology 23, 5; 10.1175/JHM-D-21-0177.1

Figure 13 shows the regressed zonal circulations along 60°N against the BBC index over the two periods before and after the mid-2000s. The figure suggests that along the propagating route over northern Eurasia, anomalous vertical motions alternately occur. However, the anomalous vertical motion centers are different over the two periods. During the period of 1987–2004, the significantly anomalous ascending and sinking motions are over 25°–55°E and 75°–100°E, respectively (Fig. 13a). During this period, the BBC pattern is related to weak anomalous vertical motion over the center of ES. In contrast, over the period of 2005–15, there are significantly anomalous ascending motions over ES (Fig. 13b), which favors more EPEs over the region.

Fig. 13.
Fig. 13.

As in Fig. 11, but against the BBC index.

Citation: Journal of Hydrometeorology 23, 5; 10.1175/JHM-D-21-0177.1

During the period of 1987–2004, the interannual variation in the ES EPE frequency is influenced by anomalous westerly water vapor transportation, which is associated with the SCAND pattern (Figs. 10c,d). During the period of 2005–15, however, the interannual variation in the ES EPE frequency is impacted by anomalous southwesterly water vapor transportation (Fig. 14a), which is now associated with the BBC pattern. As shown in Fig. 14b, the BBC-related cyclone around the Lake Baikal can lead to southwesterly water vapor transportation to ES. The pronounced results indicate that because of the interdecadal changes in BBC pattern-related atmospheric circulations, the BBC pattern can significantly influence the vertical motion and water vapor conditions over ES after the mid-2000s, consequently enhancing its relationship with the interannual variation in the ES EPE frequency over the period.

Fig. 14.
Fig. 14.

Regression maps of 9-yr high-pass-filtered, JJA-mean vertically integrated water vapor flux (vectors; kg m s−1) and its divergence (shading; 10−6 kg s−1) against (a) ES_EPE index and (b) BBC index over the period of 2005–15. (c) Regression maps of 9-yr high-pass-filtered, JJA-mean 850-hPa geopotential heights (shading; gpm) and horizontal wind (vectors; m s−1) against the ES_EPE index, from which the BBC signal has been removed over the period of 2005–15. (d) The standardized time series of the ES_EPE index (red solid curve) and EASM index (blue dashed curve) over the period of 1987–2015, and the 9-yr running correlation coefficient between these two indices. Dotted areas are significant at the 90% confidence level based on Student’s t test. Eastern Siberia is outlined by blue dashed rectangle in (a)–(c). The red dashed box in (a) is the key region for the definition of the EASM index. “A” and “C” indicate anomalous anticyclonic and cyclonic circulations, respectively. The brown line in (d) represents the threshold of the 90% confidence level for the 9-yr correlation coefficient.

Citation: Journal of Hydrometeorology 23, 5; 10.1175/JHM-D-21-0177.1

d. Enhanced influence of the East Asian summer monsoon on interannual variation in EPE frequency over ES

In addition to the BBC pattern-related southwesterly water vapor transportation to ES, the interannual variation in the ES EPE frequency is also influenced by a strong southerly water vapor channel from the western North Pacific to ES (Fig. 14a). After removing the BBC signal, strong anomalous southerly water vapor transportation from low latitudes to ES still exists (Fig. 14c), which further indicates that in addition to the influence of the BBC pattern, the East Asian circulation system could also be important for the interannual variation in the ES EPE frequency.

Over East Asia, the major atmospheric system is the summer monsoon, which is characterized by southerly winds and has a profound impact on the East Asian summer climate (e.g., Ding and Chan 2005; Wang et al. 2001; Wang 2002; Chen et al. 2004; Han et al. 2015; Wang et al. 2017; Sun et al. 2017; Tang et al. 2021). According to Fig. 14a, with southerly wind along the East Asian coast, the EASM index is defined as the 9-yr high-pass-filtered JJA-mean meridional wind averaged over the region (10°–40°N, 115°–125°E). This EASM index is correlated to that defined by Wang (2002) and Zhang et al. (1996), with coefficients of 0.94 and 0.75, respectively (significant at the 99% confidence level). The EASM index and ES_EPE index have more consistent variation over the period after the late 1990s than before (Fig. 14d), which can be confirmed by a significant increase in the 9-yr running correlation coefficients between the EASM and ES_EPE indices in the late 1990s. Over the period of 1987–97, the correlation coefficient of the two indices is only −0.26; however, it increases to 0.77 (significant at the 99% confidence level) over the period of 1998–2015.

The cause for the interdecadal change in the relationship between the EASM and ES EPE frequency is further investigated. The EASM-related 850-hPa atmospheric circulations over the two periods of 1987–97 and 1998–2015 are shown in Figs. 15a and 15b. Corresponding to the strong EASM, there is an enhanced western Pacific subtropical high (WPSH) centered in southern Japan and strong southerlies along the East Asian coast, which is a common feature in Figs. 15a and 15b. The main difference between these two panels locates in mid-to-high latitudes, especially the intensity and location of the anomalous cyclone around the Lake Baikal. Compared with the period of 1987–97, the anomalous cyclone is enhanced and extends northeastward, covering southwestern ES over the period 1998–2015. The enhanced and extended anomalous cyclone can guide the water vapor transported by the EASM further northward to ES, favoring a higher EPE frequency over the region. However, over the period of 1987–97, the anomalous cyclone around the Lake Baikal cannot transport the EASM-related water vapor to ES; consequently, the EASM has a weak relationship with the interannual variation in the ES EPE frequency over the period. Therefore, the interdecadal changes in the intensity and location of the EASM-concurrent cyclone around the Lake Baikal could have contributed to the enhanced relationship between the EASM and ES EPE frequency after the late 1990s.

Fig. 15.
Fig. 15.

Regression maps of 9-yr high-pass-filtered, JJA-mean 850-hPa geopotential heights (shading; gpm) and vertical integrated water vapor flux (vectors; kg m s−1) against the EASM index over the periods of (a) 1987–97 and (b) 1998–2015. Dotted areas are significant at the 90% confidence level based on Student’s t test. Eastern Siberia is outlined by blue dashed rectangles. “A” and “C” indicate anomalous anticyclonic and cyclonic circulations, respectively.

Citation: Journal of Hydrometeorology 23, 5; 10.1175/JHM-D-21-0177.1

5. Summary and discussion

This study first investigates the interannual variation features of the NA summer EPE frequency. REOF and regression analysis indicate that NA can be generally divided into three subregions: WS, ES, and EMNEC. To further confirm this result, similar REOF analyses on EPE frequency are applied to the datasets of GPCC and REGEN. The analysis on these two datasets also shows relatively independent variation over the three subregions (Fig. 16). Similar to the WS_EPE and ES_EPE indices, we define the EMNEC_EPE index as the 9-yr high-pass-filtered and standardized time series of regional average EPE frequency over EMNEC. The correlation coefficients between the WS_EPE index, ES_EPE index and EMNEC_EPE index in CPC-global and that in GPCC (REGEN) over the period of 1987–2014 (1987–2011) are 0.81, 0.92, and 0.97 (0.73, 0.93, and 0.96), respectively, which are all significant at the 99% confidence level. The consistent REOF results and EPE indices indicate that the dominant spatial–temporal variation features obtained from the CPC-global dataset are robust and not sensitive to the data sources. Because the variation and mechanisms of EPE frequency over NEC have been examined by previous studies (e.g., Xu et al. 2011; Du et al. 2013; Wang et al. 2013; Song et al. 2015; Wang et al. 2017; Yang et al. 2017; Cao et al. 2018; Han et al. 2019; Chen et al. 2019), the mechanisms for the interannual variations in EPE frequency over WS and ES are emphasized in this study.

Fig. 16.
Fig. 16.

As in Figs. 2d–f, but based on the data from (a)–(c) GPCC and (d)–(f) REGEN, respectively.

Citation: Journal of Hydrometeorology 23, 5; 10.1175/JHM-D-21-0177.1

A local zonal dipole pattern in the upper troposphere is important for the interannual variation in EPE frequency over WS. Anomalous cyclone to the west of WS and anticyclone to the southeast can lead to strong local divergence in the upper troposphere over WS. In the lower troposphere, there is an anomalous cyclone over WS, which can lead to local water vapor convergence and can supply more moisture. Upper-troposphere divergence and lower-troposphere convergence result in strong anomalous ascending motion over WS, according to the law of mass conservation. Through these physical processes, the local atmospheric circulations provide favorable dynamic and water vapor conditions for a higher EPE frequency over WS. The local mechanisms responsible for the interannual variation in EPE frequency over ES are similar to those over WS but with an eastward-located dipole pattern in the upper troposphere and an anomalous cyclone in the lower troposphere.

Sun (2012) have revealed that the variability of EPE precipitation explains more than 70% of the total precipitation variability over most areas of China. In this study, we find that the interannual variability of WS (ES) EPE frequency can explain 76.9% (77.6%) of the summer total precipitation variability. Such a result indicates that the variability of EPE dominates that of total precipitation over NA. The water vapor convergence and vertical ascending motion are the essential moisture and dynamic conditions for the precipitation formation. From the synoptic perspective, previous studies have revealed that both water vapor convergence and vertical ascending motion are also the necessary conditions for the EPE formation, but EPE needs more intense conditions than nonextreme precipitation event (e.g., Cao et al. 2018; Chen et al. 2019; Nie and Sun 2021). Therefore, the water vapor convergence and vertical motion are used as the local factors to explain the variation of EPE in this study. To more clearly reflect the differences in the conditions for the EPE and total precipitation over NA, some analysis on the synoptic scale is needed in the future.

In the interannual variation in WS EPE frequency, the POL and NAO patterns play important roles. The POL-related wave train pattern propagates from the North Atlantic to NA and contributes to the anomalous divergence in the upper troposphere and ascending motion over WS. Moreover, the POL pattern can enhance the water vapor convergence over WS. By modulating the local atmospheric circulations, the POL pattern provides favorable dynamic and water vapor conditions for a higher EPE frequency over WS. The NAO pattern favors water vapor transportation from the midlatitudes of the North Atlantic to WS, consequently exerting an impact on the interannual variation in EPE frequency over WS. In addition, the variations in the POL pattern and NAO pattern are independent of each other; consequently, they are independent factors for the interannual variation in EPE frequency over WS. The fitted EPE frequency using the NAO and POL indices based on the multiple linear regression method can explain 59.3% of the total interannual variation in EPE frequency over WS, further implying the dominant role of the combined influence of the POL and NAO patterns.

According to interannual variation in ES EPE frequency, the SCAND and BBC patterns play different roles over different decades. Over the period of 1987–2004, the SCAND pattern can significantly change vertical motion over ES and water vapor transportation to ES; consequently, it can influence the interannual variation in ES EPE frequency over the period. After the mid-2000s, the SCAND pattern has an interdecadal change, and the SCAND pattern-related anomalous vertical motion shifts westward; therefore, it cannot influence the variation of ES EPEs over the period. In contrast, over the period of 2005–15, the BBC pattern has an enhanced vertical motion over ES and water vapor transportation to ES; consequently, the BBC pattern has an enhanced influence on the interannual variation in the ES EPE frequency over the period. The analysis in this study suggests that the SCAND pattern and BBC pattern have an interdecadal alternate influence on the interannual variation in the ES EPE frequency approximately in the mid-2000s.

In addition to the teleconnection patterns over mid-to-high latitudes, the EASM could also influence the interannual variation in ES EPE frequency. In the connection between the EASM and ES EPE frequency, the anomalous cyclone over the Lake Baikal could play an important role. This anomalous cyclone is concurrent with the EASM, and after the late 1990s, it has an interdecadal enhancement and extends more northeastward, covering southwestern ES. These changes in the anomalous cyclone can guide the water vapor transported by the EASM further northward to ES; consequently, the EASM shows an enhanced impact on the interannual variation in the ES EPE frequency after the late 1990s. Previous studies have mainly focused on the EASM transportation of water vapor in the East Asian region (e.g., Ding and Chan 2005; Wang et al. 2001; Wang 2002; Chen et al. 2004; Han et al. 2015; Wang et al. 2017; Sun et al. 2017; Tang et al. 2021). Our analysis in this study indicates that, combining the anomalous cyclone over the Lake Baikal, the EASM can transport water vapor further northward to high-latitude regions, including ES.

In this study, we revealed some features of interannual variations in NA EPE frequency and the responsible atmospheric factors. Moreover, the interannual variations in NA EPE frequency are superimposed on the long-term increasing trend. Previous studies have indicated that the anthropogenic influence could have contribution to the increasing trend of EPE frequency (e.g., Ye et al. 2016; C. Li et al. 2020; Contractor et al. 2021; Sun et al. 2021). Therefore, the anthropogenic influence could be a factor responsible for the increasing trend of EPE frequency over NA, which should be systematically studied in the future. Meanwhile, as shown in Figs. 3a and 3b, besides the interannual variations and long-term increasing trend, the NA EPE frequencies over WS and ES also have pronounced interdecadal variations. The mechanisms for the interdecadal variations in the NA EPE frequency also deserve further research.

There are still some questions that have not been resolved in this paper. Our analysis indicates that there is an interdecadal change in the structure of the SCAND pattern and BBC pattern in the mid-2000s, which results in interdecadal changes in their influences on the ES EPE frequency. However, the mechanism responsible for the interdecadal change in these two patterns remains unclear. This study only investigates atmospheric factors, and whether the influence of some boundary forcings is important for the variation in the NA EPE frequency should also be investigated. The performance of these future studies could bring us a more comprehensive understanding of the variations in NA EPE frequency.

Acknowledgments.

This study was supported by the National Natural Science Foundation of China (Grants 41991281 and 41825010).

Data availability statement.

The datasets used in this study are freely available, and the links are listed as follows. CPC-global: https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html. GPCC: https://opendata.dwd.de/climate_environment/GPCC/html/download_gate.html. REGEN: https://doi.org/10.25914/5ca4c380b0d44. JRA-55: http://search.diasjp.net/en/dataset/JRA55.

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