1 Introduction

Monsoons and teleconnections are two important large-scale climatic phenomena that affect global precipitation and distribution of the global water resources (Christensen et al. 2013). Since the 1990s, hydrologists have paid increasing attention to the study of the relationships between these large-scale climatic factors and both surface runoff (Peng and Mysak 1993; Chiew et al. 1998; Gadgil and Sajani 1998) and groundwater level (Winograd et al. 1998; Fleming and Quilty 2006; Tremblay et al. 2011). It is well known that many large-scale climatic factors (represented by different climate indices) have strong heterogeneity at both spatial and temporal scales (Karl et al. 1999; Sun et al. 2017). Hence, understanding their characteristic scales in both space and time is crucially important to the allocation and management of local water resources.

Monsoons and teleconnections can be represented by several indices (Webster et al. 1998; Trenberth et al. 2000; Xu et al. 2015; Xiao et al. 2015). Previous works have indicated that the Indian Summer Monsoon (ISM) and East Asian Summer Monsoon (EASM) are two major monsoon indices that affect Asian precipitation (Lau and Li 1984; Wang et al. 2001). In addition, the El Niño–Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) are two important teleconnection indices that also impact Asian precipitation (Hamada et al. 2002; Chan and Zhou 2005; Zhou and Wu 2010; Xiao et al. 2015). To date, the ISM, EASM, ENSO, and PDO are considered to be primary climatic indices that contribute greatly to regional precipitation in China. Most previous studies have focused on specific regions, and an integrated and systematic assessment of the effects of monsoons and teleconnections on precipitation across China remains lacking. In particular, the timescales over which these climatic indices affect different regions of China remain unclear. In this study, we applied wavelet analysis to quantitatively evaluate the effects of four major indices (ISM, EASM, ENSO, and PDO) on precipitation within China, using precipitation data recorded during 1951–2013 at 756 meteorological stations nationwide. Detailed assessment was performed over eight climate zones with varying climate patterns. The effects of each climate index were evaluated on each climate zone to illustrate the characteristics of their spatiotemporal correlations with precipitation.

2 Data and analytical methods

2.1 Data

China is a large country covering an area of about 9.6 million km2. Although China is generally divided into six zones (such as on ClimateList) based on temperature, it is also divided into eight climatic zones based on both precipitation and climate regionalization (Zhang and Lin 1985; Xiao et al. 2013). In this work, we focus on the analysis of precipitation data and climate indices, thus, adopt the latter classification (Fig. 1): Zone I (Western arid/Semiarid), Zone II (East arid), Zone III (Northeastern China), Zone IV (Northern China), Zone V (Central China), Zone VI (Southern China), Zone VII (Southwestern China), and Zone VIII (Qinghai–Tibet Plateau). The geographical characteristics and locations of the individual climate zones, as well as the numbers of meteorological stations within each climate zone, are summarized in Table 1.

Fig. 1
figure 1

The climate zones in China and locations of meteorological stations across China (after Zhang and Lin 1985; Xiao et al. 2013)

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Table 1 Geographical characteristics and locations of the eight climate zones in China
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Monthly precipitation data recorded during 1951–2013 at the 756 meteorological stations distributed across China were used for the analysis. These data were obtained from the China Meteorological Data Service Center (http://data.cma.cn/en/). The monthly precipitation data from all stations within each climate zone were averaged to represent the regional monthly precipitation (Fig. 2). Monthly means of the ISM were obtained from the Monsoon Monitoring Page maintained by the University of Hawaii (http://apdrc.soest.hawaii.edu/projects/monsoon/). Monthly means of the EASM were collected from the National Oceanic and Atmospheric Administration (http://www.cpc.ncep.noaa.gov/products/Global_Monsoons/Asian_Monsoons/monsoon_index.shtml/). ENSO values were derived from the sea surface temperature in the Niño3.4 region (5°N–5°S, 120°–170°W), and the Niño3.4 indices were extracted from the Climate Prediction Center of NOAA (http://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/detrend.nino34.ascii.txt). The PDO index was obtained from the Earth System Research Laboratory of NOAA (http://www.esrl.noaa.gov/psd/data/correlation/pdo.data/).

Fig. 2
figure 2

The monthly precipitation in the eight zones

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2.2 Analytical methods

Wavelet coherence and global coherence were used to characterize the relationships between regional monthly precipitation and the four selected climate indices: the ISM, EASM, ENSO, and PDO.

2.2.1 Wavelet coherence

Wavelet coherence is an approach used for analyzing the degree of coherence of cross wavelet transform in time–frequency space. Following Torrence and Webster (1999), the wavelet coherence coefficient can be defined as follows (Grinsted et al. 2004; Hao et al. 2016):

$$R^{2} (a,\tau )\; = \;\frac{{\left| {S\left( {a^{{ - 1}} W_{{xy}} (a,\tau )} \right)} \right|^{2} }}{{S\left( {a^{{ - 1}} \left| {W_{x} (a,\tau )} \right|^{2} } \right) \cdot S\left( {a^{{ - 1}} \left| {W_{y} (a,\tau )} \right|^{2} } \right)}}$$
(1)

where \({R^2}\left( {\alpha ,\tau } \right)\) takes values between 0 (no coherency) and 1 (perfect coherency), α is the scale expansion parameter, τ is the dimensionless time-shift parameter, \({W_{xy}}\left( {\alpha ,\tau } \right)\) is the cross wavelet transform of the two time series, Wx and Wy are the sums of ranks of observations in samples \({x_t}\) and \({y_t}\), respectively, and S represents a smoothing operator, which is defined as:

$$S\left( W \right)={S_{scale}}\left( {{S_{time}}\left( {W\left( {\alpha ,\tau } \right)} \right)} \right)$$
(2)

where Sscale and Stime represent smoothing along the wavelet scale axis and in time, respectively.

The definition of Eq. (1) is similar to that of a traditional correlation coefficient, and it allows consideration of wavelet coherence as a localized correlation coefficient in time–frequency space. Referring to Torrence and Compo (1998), a suitable smoothing operator for the Morlet wavelet can be determined with the following equation:

$$\begin{gathered} {S_{time}}\left( W \right){|_\alpha }=\left( {W\left( {\alpha ,\tau } \right) \cdot c_{1}^{{\frac{{ - {t^2}}}{{2{\alpha ^2}}}}}} \right){|_\alpha }, \hfill \\ \hfill \\ {S_{time}}(W){|_\tau }=\left( {W\left( {\alpha ,\tau } \right) \cdot {c_2}\Pi \left( {0.6\alpha } \right)} \right){|_\tau } \hfill \\ \end{gathered}$$
(3)

where \({c_1}\) and \({c_2}\) are normalization constants and π is the rectangle function. The factor of 0.6 is an empirically determined scale decorrelation length for the Morlet wavelet (Torrence and Compo 1998). In practice, both convolutions are processed discretely and thus the two normalization constants are numerically determined.

The statistical significance level of wavelet coherence was estimated through Monte Carlo simulations, which require more than 1000 surrogate dataset pairs with the same first order autoregressive coefficients as the input datasets. The significance level for each scale was estimated using only the values outside the Cone of Influence, which is the region of the wavelet spectrum in which edge effects become important (Torrence and Compo 1998). The number of scales per octave should be sufficiently high to capture the rectangle shape of the scale smoothing operator while minimizing computing time. An empirically satisfactory parameter, i.e., 12 scales per octave, was used with reference to Torrence and Compo (1998). In addition, periodicities different from noise that were significant at the 5% level, i.e., coherence for which the confidence level was > 95%, were analyzed in this study.

2.2.2 Global coherence

With reference to Partal and Kucuk (2006), the global wavelet coherence coefficient at scale α can be defined as time-averaged wavelet coherence coefficients:

$${\bar {R}^2}\left( \alpha \right)=\frac{1}{n}\mathop \Sigma \limits_{{\tau =1}}^{n} {R^2}\left( {\alpha ,\tau } \right)$$
(4)

where n is the number of points in the time scale.

The global coherence coefficient can be used to evaluate the correlation between two time-series at different scales while neglecting the influence of time. This parameter is useful for examining the characteristic of periodicities (Torrence and Compo 1998; Labat 2010). A cross wavelet and wavelet coherence toolbox for MATLAB, downloaded from http://grinsted.github.io/wavelet-coherence/, was used for this analysis (Grinsted et al. 2004).

3 Results

3.1 Wavelet coherence

3.1.1 Wavelet coherence between monthly precipitation and the ISM

Figure 3 shows the wavelet coherence between monthly precipitation and the ISM in the eight climate zones with phase lags between components as illustrated by black arrows (the same hereafter). High wavelet coherence (larger than 0.8) is observed at the annual scale, indicating the dominant effect of the ISM on precipitation across entire area of China on the annual timescale. In addition, relatively high wavelet coherence (0.6–0.8) is observed for the periodicity of intra-annual (i.e., 0.5–1.0 year) and inter-annual scales (i.e., > 2.0 but < 10.0 years) in different climate zones and in different years. For example, the effects of the ISM are important in Zones I, II, III, IV, VI, and VIII on timescales of between 0.5 and 1.0 year (Fig. 3a–d, g, h). However, the effects of the ISM on the inter-annual timescale are intermittent, as demonstrated by the different sizes of the patches of high coherence in different years. Moreover, the effects of the ISM on the decadal timescale (i.e., > 10.0 years) are also important in a few zones, particularly Zones I and V.

Fig. 3
figure 3

Wavelet coherence between monthly precipitation and the ISM: a zone I, b zone II, c zone III, d zone IV, e zone V, f zone VI, g zone VII, and h zone VIII

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3.1.2 Wavelet coherence between monthly precipitation and the EASM

Figure 4 shows the wavelet coherence between monthly precipitation and the EASM in the eight climate zones. Similar to the effects of the ISM, strong and continuous wavelet coherence is observed for the periodicity of 1.0 year in all eight climate zones. However, the effects of the EASM on the intra-annual timescale (i.e., 0.5–1.0 year) are weaker than the ISM in most zones. The effects of the EASM on the inter-annual timescale are also intermittent in all eight zones. On the decadal timescale, the effects of the EASM are observed in most zones but the strength of the coherence varies considerably. The strength of the coherence is significantly different interannually, indicating strong spatiotemporal variability.

Fig. 4
figure 4

Wavelet coherence between monthly precipitation and the EASM: a zone I, b zone II, c zone III, d zone IV, e zone V, f zone VI, g zone VII, and h zone VIII

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3.1.3 Wavelet coherence between monthly precipitation and the ENSO

Figure 5 shows the wavelet coherence between monthly precipitation and the ENSO in the eight climate zones. The influence of the ENSO is very different from that of the monsoon indices (i.e. ISM and EASM). No strong and continuous effect is evident for the periodicity of 1.0 year. Statistically, annual-scale coherence has no significant difference from the coherence on timescales of < 1.0 year. The intermittency of other scales is also evident in all climate zones, although high coherence is observed on different timescales in different zones and during different years. For instance, the data shows that the inter-annual scale coherence (e.g., 2.0–8.0 years) is high in Zones I, II, V, and VIII (Fig. 5a, b, e, h), while the decadal-scale coherence (e.g., 10.0–16.0 years) is high in Zones III and V (Fig. 5c, e).

Fig. 5
figure 5

Wavelet coherence between monthly precipitation and the ENSO: a zone I, b zone II, c zone III, d zone IV, e zone V, f zone VI, g zone VII, and h zone VIII

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3.1.4 Wavelet coherence between monthly precipitation and the PDO

Figure 6 shows the wavelet coherence between monthly precipitation and the PDO in the eight climate zones. Similar to the ENSO, there is no evident continuous coherence between precipitation and the PDO on the annual timescale over the entire period of study. The coherence on the annual timescale is slightly higher than that associated with timescales of < 1.0 year. In addition, the PDO also has considerable influence on both inter-annual and decadal timescales, similar to the ENSO. Again, these influences are also concentrated on certain timescales and during certain years.

Fig. 6
figure 6

Wavelet coherence between monthly precipitation and the PDO: a zone I, b zone II, c zone III, d zone IV, e zone V, f zone VI, g zone VII, and h zone VIII

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3.2 Global coherence between monthly precipitation and climate indices

Figure 7 shows the global coherence for all four climate indices in all eight zones, which provides an evaluation of averaged coherence between monthly precipitation and each climate index over different timescales. This analysis is yearly independent, and plotting all indices together enable the comparison of the relative coherence significance of each index in each zone at all timescales.

Fig. 7
figure 7

Global coherence between monthly precipitation and the four climate indices in the eight climate zones: a zone I, b zone II, c zone III, d zone IV, e zone V, f zone VI, g zone VII, and h zone VIII (black: ISM, green: EASM, blue: ENSO, red: PDO)

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Strong global coherence with monthly precipitation is observable on the timescale of 1.0 year for both the ISM and the EASM in all eight zones, indicating the dominant effects of monsoons across China. Relatively strong global coherence is also found on 0.5 year timescale for the monsoon indices, particularly for the ISM in Zones I, II, III, IV, VII, and VIII (Fig. 7a–d, g, h).

On timescales other than annual and intra-annual, the effects of monsoons and teleconnections are more localized (such as the strong ENSO influence on timescales of 12.0–16.0 years in Zone III and on timescales of 3.0–5.0 years in Zone VIII). These localized effects demonstrate the variability of monthly precipitation as the result induced by large-scale climate indices. In some regions, monthly precipitation has similar coherence with all indices (such as in Zone VII on timescales > 2.0 years). These results demonstrate that the effect of any particular climate index is not significant on large timescales.

4 Discussions

4.1 Monsoons

It is well known that the Asian summer monsoons (i.e., the ISM and the EASM) dominate the hydrometeorological processes in most Asian regions (Tao and Chen 1987; Huang et al. 1998). Mainland China extends from South Asia to East Asia, which constitutes a large area influenced by the Asian monsoons. Both the ISM and EASM have been found to correlate with climate in China (Zhou and Cheng 1987; Zhou and Jia 2003; Zhang and Pu 2004; Xu and Qian 2006), and the effects of the monsoons vary on different spatiotemporal scales (Liu and Ding 2008b; Ding et al. 2013; Liu and He 2015; Li et al. 2016).

4.1.1 Correlation between monthly precipitation and the ISM

The ISM is the strongest monsoon system in the world. Our results show that the ISM has profound correlation with monthly precipitation on various timescales, especially on the annual timescale, across China (Figs. 3, 7).

On intra-annual timescales, the effect of the ISM on monthly precipitation in China varies among the different climate zones; the ISM mainly affects precipitation in Zones II, III, IV, and VIII (Fig. 7b–d, g, h). Areas of high correlation are located in a belt from the Qinghai–Tibet Plateau to Northeastern China, which has been defined as the North Branch of the monsoon by Liu and Ding (2008a). The atmospheric heat source of the Indian low pressure area, together with the Asian summer monsoon trough, enhances the formation of water vapor, and the southwesterly winds in the low latitudes drive the water vapor northeastward. During this process, the Indian low pressure area promotes extension over the Western Pacific ridge, and water vapor from the Western Pacific Subtropical High joins the North Branch of the monsoon (Liu and Ding 2008b). Consequently, the transport of water vapor extends to Northern China (Zone IV) and Northeastern China (Zone III), increasing precipitation from the Qinghai–Tibet Plateau (Zone VIII) to Northeastern China (Zone III) (Guo and Wang 1988; Kripalani and Singh 1993; Zhang et al. 1999, 2016; Liu and Ding 2008a; Ding and Wang 2005).

On annual timescales, the ISM strongly affects monthly precipitation in all eight climate zones (Figs. 3, 7). This agrees with the widely reported influence of the ISM observed across broad and different regions of China (Zhou and Cheng 1987; Kripalani and Singh 1993; Kripalani and Kulkarni 2001; He et al. 2005; Hu et al. 2010; Li et al. 2016; Zhang et al. 2016).

On longer timescales (i.e., decadal and interdecadal), the ISM mainly affects the monthly precipitation in Zones I and V (Figs. 3a, e, 7a, e). The region of Xinjiang (Zone I) is bordered by the Qinghai–Tibet Plateau to the south and by the Iran Plateau to the southwest, which prevent southward transport of water vapor. Xinjiang (Zone I) is known as an arid/semiarid region where overall precipitation is low. However, on the decadal timescale, the Somali jet stream delivers water vapor across the equator and over the Xinjiang (Zone I) area because of the effects of the dipole between the Arabian Sea anticyclone and the Iran cyclone (Yang 2003; Zhou and Jia 2003; Zhang and Deng 1987). This climatic configuration increases the humidity, which affects precipitation in Xinjiang (Zone I) on the decadal timescale. For Zone V, the South Asian High is strongly correlated with the Western Pacific Subtropical High, which influences precipitation in the Yangtze–Huaihe River basin on the timescale of 10–15 years (Zhao et al. 2003; Chen et al. 2011). Precipitation in this basin has distinct variation on the decadal timescale (Zhang and Wu 2001; Qian et al. 2002; Zhang et al. 2002). The influence of the ISM in the middle–lower Yangtze Plain (Zone V) is likely due to the impact of the ISM on the high-altitude atmospheric circulation (Dai et al. 2002). When the ISM strengthens, the 500-hPa geopotential height (contours) increases over East Asia and the Western Pacific Subtropical High extends toward the north and west. Consequently, the westerlies move northward and eastward, bringing large amounts of water vapor from the lower latitudes to the middle–lower Yangtze Plain (Zone V) and promoting precipitation (Liu and Ding 2008c).

4.1.2 Correlation between monthly precipitation and the EASM

The EASM system consists of various features including the Australian High, cross-equatorial flow at 105°E, West North Pacific Subtropical High, Mascarene High, southwest summer monsoon in the South Sea, Intertropical Convergence Zone (ITCZ), and Meiyu front at 30°N (Tao and Chen 1987; Huang and Tang 1987). Figures 4 and 7d–f illustrate that the EASM mainly affects precipitation in Zones IV, V, and VI (i.e., eastern China) on inter-annual timescales. Previous studies have shown that precipitation in the eastern China is strongly correlated with the Southern Hemisphere circulation (Gao and Wang 2007; Sun et al. 2013). As an important atmospheric circulation feature of the EASM system, the Australian High has significant impact on the climate of eastern China on inter-annual timescales (Huang and Tang 1987; He et al. 1991; Chen et al. 2005; Teng et al. 2005). The Australian High typically strengthens the equatorial westerlies and winds over the ITCZ, prompting the Western Pacific Subtropical High to move southwestward (Zhang et al. 2010). This atmospheric circulation can deliver water vapor to eastern China and produce precipitation. Simultaneously, induced by the effects of the Australian High, the cross-equatorial flow at 105°E can become significantly enhanced and enter the South China Sea, strengthening the tropical monsoons. This combined process can increase water vapor transport and contribute to precipitation in eastern China (Chen and Wu 1998).

The most obvious feature of the EASM is its annual scale impact on monthly precipitation in all eight climate zones (Figs. 4, 7). This profound impact has also been reported by Guo (1985), Yu et al. (2009), and Zhang et al. (2011), although these previous studies were limited to specific regions of China.

On decadal and inter-decadal timescales, the results demonstrate that the EASM mainly affects precipitation in Zone I (Xinjiang), Zone V (Yangtze–Huaihe river Plain), and Zone VIII (Qinghai–Tibet Plateau) (Figs. 4, 7). This observation is consistent with the reports in the Yangtze River region by other researchers (Guo 1983; Wang et al. 2001; Lu et al. 2004; Xu et al. 2008; Yu et al. 2009). This decadal-scale correlation is due to the movement of the EASM on the same timescales that affect the precipitation of East China (Lu et al. 2011). The movement usually affects the middle–lower Yangtze Plain from the north (Lu et al. 2011). The Western Pacific Subtropical High moves southwestward, resulting in the appearance of the Meiyu front over the subtropical region of East Asia, which largely affects precipitation in Zone V (Lu et al. 2004; Wang et al. 2001; Zhang and Tao 1998; Chen and Xue 2013). The equatorial westerly winds of the Indian Ocean move eastward through the South China Sea, transporting large volumes of water vapor from the Indochina Peninsula and the South China Sea toward the Yangtze River basin in China, which promotes heavy precipitation in the middle–lower Yangtze Plain (Zhou and Huang 2003; Wang and Kiyotoshi 2005).

On the decadal scale, the EASM also affects precipitation in the Western arid/Semiarid area (Zone I) because of the Mascarene High and the Western Pacific Subtropical High (Wang and Yang 2008; Chen et al. 2012). The Mascarene High causes the trade winds to cross the equator, forming the so-called Somali cross-equatorial flow (Cui et al. 2008). This cross-equatorial flow, which represents the major passage of water vapor between the Southern and Northern hemispheres, primarily affects precipitation in Zone I (Xinjiang) (Li et al. 2010). In addition, when the Western Pacific Subtropical High strengthens and moves southward, it also causes the transport of water vapor from the western North Pacific Ocean to Zone I (Xinjiang). This decadal-scale influence (Figs. 4, 7, and Wang and Xue 2003) is due to the decadal variation of East Asian–Pacific teleconnection (Yu et al. 2009; Chen et al. 2012).

A strong EASM reflects a strong atmospheric heat source over the South China Sea and the tropical western Pacific Ocean. If the atmospheric heat source over the Qinghai–Tibet Plateau (Zone VIII) were weak, an atmospheric heat gradient would form (Xu et al. 2016b). As a result, the Western Pacific Subtropical High would be strengthened, which could affect the eastern regions of China (Zhou et al. 2005b). This process would promote the movement of water vapor toward the west, transporting it toward the Qinghai–Tibet Plateau and increasing precipitation in Zone VIII (Zhou et al. 2005a, 2012; Lin et al. 2016).

4.2 Teleconnections

4.2.1 Correlation between monthly precipitation and the ENSO

The ENSO and the Asian monsoon are interactive (Fu 1985; Xu and Zhu 2005; Zhu et al. 2007; Xu et al. 2016a). Although previous research has shown that the ENSO is one of the major climate indices that affect precipitation in China on inter-annual timescales (Ding 1993), our analyses (Figs. 5, 7a, h) illustrate that, on such timescales, the ENSO mainly affects precipitation in Zones I and VIII, in which Xinjiang and the Qinghai–Tibet Plateau are located, respectively. When La Niña occurs, the Indian High becomes strengthened, which enhances the Somali cross-equatorial flow, causing increased precipitation in Xinjiang in Zone I. Conversely, when El Niño occurs, precipitation in Xinjiang decreases (Chen et al. 2005). In addition, when El Niño occurs, a cyclone appears over the Indian Ocean and the South Asian High strengthens, causing increased precipitation over the Qinghai–Tibet Plateau in Zone VIII (Zhou et al. 2000). The occurrence of La Niña induces the opposite effects in the respective zones.

In the case of El Niño, the sea surface temperature of the tropical eastern Pacific Ocean is high, which results in weakened Walker and Hadley circulations. The enhanced Western Pacific Subtropical High expands southward, which transports water vapor to the Northeastern China (Zone III) and the Northern China (Zone V) regions (Chen 1977; Zou and Ni 1997). This is consistent with our results that indicate that the ENSO has very strong effects on Zones III and V on decadal timescales (Fig. 7), and has relatively strong effects on other timescales, although the area of influence in Zone V is relatively narrow (Fig. 5).

4.2.2 Correlation between monthly precipitation and the PDO

Correlation results demonstrated that the PDO mainly affects precipitation in Zone I on inter-annual timescales (Figs. 6, 7a). In the Northern Hemisphere, variations of precipitation and climate are known to be controlled by the EASM and the westerly circulation (An and Chen 2009; Li et al. 2015). Previous discussion has demonstrated the strong effects of the EASM on overall precipitation in China. The effects of the PDO on regional precipitation are mainly attributable to alterations of the intensity of the EASM and the westerly circulation (Gan 2000; Yang et al. 2004; Ma and Shao 2006; Li et al. 2009; He and Jiang 2011).

When the PDO is in its positive phase, the Mascarene High and the Australian High become enhanced and cross-equatorial flow strengthens, resulting in increased precipitation in Xinjiang (Zone I) (Wang and Yang 2008). The westerly circulation can also be strengthened by the PDO. Water vapor transported toward Xinjiang by the westerly circulation can enhance precipitation in Zone I because of orographic effects as the air moves over the mountains (Xu et al. 2009).

On decadal or inter-decadal timescales, the PDO mainly affects precipitation in Zones II and VI, which encompass Inner Mongolia and southern areas of China (Figs. 6, 7b, f). The PDO index represents the thermal difference over the North Pacific Ocean, which affects the climate of the Northern Hemisphere with decadal-scale periodicity (Hurrell and van Loon 1997).

The water vapor associated with precipitation over Inner Mongolia (East arid, Zone II) in summer is derived mainly from the South China Sea and the Bay of Bengal (Wu et al. 2012). When the PDO is in its positive phase, both the westerly jet and the Western Pacific Subtropical High move northward; thus, Inner Mongolia becomes influenced by the westerly circulation. There are no mountains in central eastern parts of Inner Mongolia. This means large quantities of water vapor from the Bay of Bengal, South China Sea, and western Pacific Ocean can be transported to the Hetao belt and Inner Mongolia (East arid, Zone II), promoting local precipitation (Qu et al. 2004; Xu et al. 2009; Li et al. 2011).

When the PDO is in its positive phase, sea surface temperature in the middle of the North Pacific Ocean is low and the Aleutian Low is weak, which causes high pressure over Northeast Japan and low pressure over the Qinghai–Tibet Plateau. This synoptic situation results in an area of vertical ascent over Southern China (Zone VI), which is advantageous to water vapor convergence and increased precipitation. At the same time, the sea surface temperature of the equatorial Pacific Ocean will become colder, which leads to an anticyclone over the Philippines and water vapor transport from the South China Sea to Southern China (Zone VI) (Cheng et al. 2016). Moreover, when a positive PDO phase is combined with El Nino, an anticyclonic circulation will develop in the northwestern Pacific Ocean. Associated southerly winds will enhance the southwesterly warm wet flow, increasing the transport of water vapor toward southern China. Meanwhile, the Western Pacific Subtropical High will become strengthened and move westward. This also acts to enhance the southwesterly airflow and the transport of water vapor from the South China Sea toward South China (Zone VI) (Li et al. 2010; Gu 2008).

5 Conclusions

To obtain better understanding of the effects of large-scale climatic phenomena on precipitation across eight climate zones of China, this study applied wavelet coherence and global coherence in the acquisition of correlations between monthly precipitation and both monsoons (the ISM and the EASM) and teleconnections (the ENSO and the PDO) on different timescales. This work illustrated new results on the timescale of the influences of these climate indices on the precipitation in different regions of China, which is important for both management of water resources and the prediction of precipitation with consideration of future climate change across China.

The major conclusions derived are outlined in the following.

  1. 1.

    On the annual timescale, monsoons have stronger effects than teleconnections on monthly precipitation in all eight climate zones of China.

  2. 2.

    On the intra-annual (0.5–1 year) and inter-annual (2–10 year) scales, the ISM mainly affects precipitation in the East Arid Region, Northeastern China, Northern China, and Qinghai-Tibet Plateau; the EASM mainly affects Northern China, Central China, and Southern China; the ENSO mainly affects Western Arid/Semiarid region and Qinghai-Tibet Plateau; the PDO mainly affects the Western Arid/Semiarid region.

  3. 3.

    On the decadal timescale, the ISM mainly affects the Western arid/Semiarid and Central China; the EASM mainly affects Western arid/Semiarid, Central China, and Qinghai–Tibet Plateau; the ENSO mainly affects Northeastern China and Central China; and the PDO mainly affects East arid and Southern China regions.