The Third Pole, commonly referred to as the “Asian Water Tower”, provides critical freshwater supplies to neighbouring countries (Yao et al., 2012; Wang et al., 2021). This region is centered on the Tibetan Plateau (TP), and includes the Pamir Mountains, Hindu Kush, Hengduan Mountains, Kunlun and Qilian Mountains and the Himalayas, with a total area of over 5×10⁶ km² and an average elevation of over 4000 m a.s.l. (Figure 1a). The Third Pole region is unique and has a significant impact on the climate of China, the Northern Hemisphere, and even the globe, as well as environmental systems over long and large spatio-temporal scales (Yao, 2014). From the perspectives of natural science, social development, and national security, the Belt and Road Initiative has recently drawn even more attention to the Third Pole.

As reported by Yao et al. (2018), the Third Pole is primarily influenced by the synergy of the Asian monsoon and the mid-latitude westerlies. This region is highly sensitive to climate change, which can exert major controls on the circulation at both regional and continental scales. Meteorological records have shown that the TP has warmed by 0.3- 0.4℃/10a over the past five decades, which is twice the rate observed globally (Chen et al., 2015). P has generally increased across the TP over the past five decades, with the region becoming wetter in the north and drier in the south (Chen et al., 2015). P dynamics affect surface hydrology and water resources, such as the expansion and contraction of lake systems (Lei et al., 2014) and variations in discharge (Cuo et al., 2014; Yang et al., 2014). The role of P variation, especially in relation to seasonal characteristics, is crucial for local ecosystems. As an example, recent decreases in spring P in the southwestern TP have caused phenological delays (Shen et al., 2014, 2015). Furthermore, P caused by the mesoscale convective system in the eastern and central-southern regions of the Third Pole is indicative of different land surface heating conditions across the region, due to the different vertical structures of mesoscale convective system (Sugimoto and Ueno, 2010; Zeng et al., 2016).

P variability is complex because it is spatio-temporally diverse and subject to both gradual and abrupt changes. To date, many studies have been conducted in terms of P trends over different timescales and in different subregions of the Third Pole (e.g., Cuo and Zhang, 2017; Ge et al., 2017; Zhou et al., 2018). Variation in the mode of variability can be used to detect inherent properties in data derived from hydrological and meteorological records. For example, low-frequency variability in runoff can be inferred from P data (Markovic and Koch, 2014). Understanding the mode of variability can also increase the potential for long-term stochastic modelling and the predictability of hydroclimatic time series, while also being important for long-term engineering-based planning (Brandimarte et al., 2011).

P variability can be attributed to the large-scale circulation, which strong influences the local weather and climate over the Third Pole. The correlation relationship between P and climate indices is often used to easily and quickly find the possible circulations that influence the spatial and temporal changes of P (e.g., Cuo et al., 2013). The artificial neural network system of the self-organising map can also be used to detect the relationship between the large-scale circulation and regional P in the eastern and central TP (Liu et al., 2016). The distinct large-scale circulations and their evolution associated with different summer rainfall periodicity detected by the power spectrum could mechanistically reveal how the large-scale circulation affects P in the eastern TP (Yang et al., 2017).

The oxygen isotopic composition in P/river water is an integrated tracer of circulation processes worldwide and can determine moisture sources (Yao et al., 2009; Yao et al., 2013). For example, the oxygen isotopic composition in P over the TP is mainly controlled by changes in water vapour sources which are ultimately caused by northward and southward shifts in the Westerly circulation (Liu et al., 2015). According to the spatial and temporal patterns of P δ¹⁸O and its relationships with temperature and P, the TP can be divided into three distinct domains, associated with the influence of the westerlies (northern region), the South Asian Monsoon (hereafter referred to as the Indian monsoon, southern region), and the transitional domain in between, respectively (Yao et al., 2013, Figure 1a). The study by Gao et al. (2018) demonstrated that in the Indian monsoon domain, the El Niño-Southern Oscillation (ENSO) plays an important role in the annual variability of summer P stable isotopes by influencing the Indian summer monsoon and subsequent moisture transport. Further research into the links between the mode of P variability and the large-scale circulation will help to understand the mechanical causes.

Therefore, variations in the mode of annual P variability and its links to the large-scale circulation, combined with different domains through the spatial and temporal patterns of oxygen isotopic composition in P and their relationships with temperature and P, could improve our understanding of P variability and its mechanical causes across the Third Pole region. Based on the spatio-temporal characteristics of P, the objectives of this study were: (i) to identify significant interannual periods of P and their timing from meteorological station data; and (ii) to investigate whether modes of variability are triggered by large-scale circulation using the relationship between P and seven circulation indices. The aim of this study is to provide a comprehensive summary of spatio-temporal P variability and to establish correlations between the scaling properties of P and the large-scale circulations, which is important for hydrological science and water resource management.

The Asian monsoon and the mid-latitude westerlies act synergistically over the Third Pole. In addition to water vapour from local recirculation, water vapour on the Third Pole comes mainly from the southwestern source area controlled by the Indian summer winds, i.e., water vapour from the Arabian Sea, the Indian Ocean and the Bay of Bengal is transported from the southern boundary to the Third Pole. In addition, the source areas of water vapour on the TP include the western source area controlled by the westerlies and the southeastern source area controlled by the Asian monsoon (Li and Pan, 2022). Using the δ¹⁸O in P over the TP, Yao et al. (2013) suggested that (1) the stations with enriched summer δ¹⁸O correspond to the region, north of 35°N, dominated by the westerlies, and depict a close link between δ¹⁸O and local temperature and weak relationships with P amount; (2) the stations with clear summer depletion correspond to the monsoon region, south of 30°N, dominated by the Indian monsoon, and show an antiphase between δ¹⁸O and P amount; and (3) the regions where seasonal cycles show more complicated δ¹⁸O variations (located between 30°N and 35°N) are defined as the transitional domain, suggesting shifting influences between the westerlies and the Indian monsoon. The East Asian Summer Monsoon enters the Third Pole from northern part of the eastern border and affects climate in the Qaidam Basin (the northeast of the Third Pole), which was also demonstrated by high correlation between summer P and East Asian Summer Monsoon index in the Qaidam Basin (Gao and Niu, 2018). Here, the westerlies domain was further divided into two sub-domains by the western boundary of the Qaidam Basin, referred to as the western westerlies domain and the eastern westerlies domain. Therefore, the Third Pole was dived into four domains in this study.

Monthly P data were collected from meteorological stations. Data from China were provided by the National Climate Center of the China Meteorological Administration (CMA; http://data.cma.cn/data/cdcindex/cid/6d1b5efbdcbf9a58.html), while data from other Third Pole regions were downloaded from the National Oceanic and Atmospheric Administration (NOAA; https://www.ncdc.noaa.gov/cdo-web/datasets). For this study, data from meteorological stations above 1000 m a.s.l. with more than 10 years of observations were selected. All selected meteorological stations had a data coverage of approximately 99.9%. Missing data were interpolated using same-month averages over a 10-year period. In total, 417 meteorological stations were selected for this study (see Table S1 in Supplementary Materials for details). For the Third Pole, about 65% of the meteorological stations had more than 30 years of observations, while 35% had more than 60 years (Figure 1a). More than half of China’s meteorological stations have more than 60 years of observations. Although the start and end dates of observations varied, this study used all available P data from the 417 meteorological stations to take full advantage of data specific to the Third Pole, which has been largely underreported. More than half of these meteorological stations cover the period from 1961-1990 (Figure 1b) and were used in this study to compare the timing of P periodicity over the Third Pole and its corresponding correlation with the large-scale circulations in different domains. Meteorological stations in Kashmir (i.e., the southwestern region of the Third Pole) and the southern slopes of the Himalayas were not used in this study due to short data records.

The westerlies and the Asian monsoon bring water vapour into the Third Pole and synergistically controlled the P change. The synergy of the westerlies and the monsoon makes different kinds of circulation patterns over the Third Pole. The dominant circulation patterns can cause the increase of P by increasing P days and daily P. The increase in the number of dominant circulation patterns (its ratio to the total number of patterns) could lead to increased P. The variability of the sea surface temperature field and the atmospheric circulation mode indirectly influence the precipitation change by externally driving the westerlies and the Asian monsoon. Here, seven large-scale circulation indices were used to investigate the links between P periodicity and large-scale circulations in the different domains of the Third Pole, including the Westerly Jet (WJ), the East Asian Summer Monsoon (EASM), and the South Asian Monsoon (SAM) indices as well as the El Niño-Southern Oscillation (ENSO, the Niño 3.4 index, 120°-170°W, 5°S-5°N), the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO) and the Indian Ocean Dipole (IOD). The relationship between P and all circulation indices is shown in Table 1. Westerly Jet index is Eurasian Zonal Circulation Index (January-December) at 500 hPa within 45°-65°N and 0°-150°E. The EASM index is defined as an area-averaged seasonally (June-August) dynamical normalized seasonality at 850 hPa within the East Asian monsoon domain (10°-40°N, 110°-140°E), and the SAM index is defined as an area-averaged seasonally (June-September) dynamical normalized seasonality at 850 hPa within the South Asian domain (5°-22.5°N, 35°-97.5°E) (Li and Zeng, 2002). The ENSO is monthly SST anomalies in the Niño 3.4 region (5°N-5°S, 120°-170°W), with 1981-2010 mean removed. The AO is defined as the leading mode of Empirical Orthogonal Function (EOF) analysis of monthly mean height anomalies at 1000 hPa poleward of 20°N. The NAO index is defined as the monthly normalized pressure difference between Gibraltar and southwest Iceland, and then normalized by the standard deviation of the monthly index (1979-2000 base period). The NAO index is defined by applying the Rotated Principal Component Analysis (RPCA) to monthly standardized 500 mb height anomalies obtained from the CDAS within 20°-90°N between January 1950 and December 2000. The strength of IDO is measured by the summer Dipole Mode Index (DMI) which is represented by anomalous SST gradient between the western equatorial Indian Ocean (10°S-10°N, 50°E-70°E) and the south eastern equatorial Indian Ocean (10°S-0°, 90°E-110°E). The Westerly Jet (WJ) was downloaded from the National Climate Center of the China Meteorological Administration (https://cmdp.ncc-cma.net/Monitoring/cn_index_130.php). The East Asian Summer Monsoon (EASM) and South Asian Summer Monsoon (SAM) indices were provided by Li and Zeng (2002). The El Niño-Southern Oscillation (ENSO, the Niño 3.4 Index, 120°-170°W, 5°S-5°N), the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), and the Dipole Mode Index (DMI) were downloaded from the NOAA ESRL Physical Sciences Laboratory (https://psl.noaa.gov/gcos_wgsp/Timeseries/).

Periodicity transforms of climate variables are complex, and multiple periodicity scales can coexist in the same period. For this study, the Morlet wavelet method was used to identify the scales of significant (p<0.05) variability within the time series data and the temporal evolution of each frequency. This method provides the most reliable means of detecting variability in periodic geophysical signals across different time scales (Rigozo et al., 2002; Liang et al., 2010). In this study, the MATLAB wavelet package developed by Torrence and Compo (1998) (available online at http://paos.colorado.edu/research/wavelets/) was used. The Morlet wavelet has a Gaussian modulated plane wave construction (Torrence and Compo, 1998):

where ω₀ is the dimensionless frequency (a value of 6 was used in this study to meet the admissibility condition) (Farge, 1992).

The wavelet function is expressed as:

where s is the wavelet scale; ω is the frequency; and H(ω) is the Heaviside step function where H(ω) = 1 if ω > 0, otherwise H(ω) = 0. A chi-squared test (χ²) was used to determine the statistical significance of the resulting wavelet spectrum against the red noise hypothesis, following Torrence and Compo (1998).

In this study, two main outputs of the wavelet transform were used; the global wavelet spectrum (GWS) and the scale-averaged wavelet spectrum (SAWS). The normalisation and standardisation of the GWS follow methods described by Torrence and Compo (1998) and Markovic and Koch (2014). The GWS divided by the variance of the analysed time series is the normalised global wavelet spectrum (nGWS). The standardised nGWS (between 0 and 1) was used to detect P periods within the different domains and is calculated as follows:

The SAWS can be interpreted as a time series of the variance of the analysed data within a selected scale band. This method has been used to investigate the mechanisms linking the P periodicity to the large-scale circulations of the different domains.

Two or more base periods can form a larger period. In this study, four sets of periodicities were detected, each consisting of three multiple time sets (as shown in Table 2).

As shown in Figure 2, high annual P (>800 mm) was observed in the humid southeastern (Hengduan Mountains and the Yunnan-Guizhou Plateau, i.e., the Indian monsoon domain) and humid northwestern (the western slopes of the Pamir Mountains and the northern slope of the Tianshan Mountains, i.e., the western westerlies domain) regions of the Third Pole. Low annual P (<200 mm) was observed in the dry westerlies domain, which includes deserts and basins. A general increasing trend in P was observed at 60% of the meteorological stations during the period 1961-1990. Increasing P occurred in the transitional domain and parts of the westerlies domains. In this study, a significant (p<0.05) increasing P trend was observed at 17 meteorological stations, of which the annual P of 12 meteorological stations (70%) had less than 640 mm. In addition, we observed high increasing P trends (>10 mm a^-1) at three meteorological stations in the Indian monsoon domain (>800 mm). Decreasing P trends were mainly observed in the westerlies and Indian monsoon domains. Five stations showed a significant (p<0.05) decreasing trend, four of which had annual P greater than 740 mm.

In order to best reveal the P periodicity, especially in the southwestern region of the Third Pole where P records are mostly short, the Morlet wavelet transform method was applied to the data obtained from all meteorological stations with P records longer than 10 years. Significant (p<0.05) periods are shown in Figure 3. The quasi-periods varied from 2 to 11 years, and this was also the case for records from meteorological stations with long-term data. In this study, the low-frequency variability mode (7-11 years quasi-periods) was rare. Furthermore, 11-year quasi-periods were only detected at two meteorological stations in the Indian monsoon and the western westerlies domains, respectively. In addition, a 10-year quasi-period was only detected at a single meteorological station in the western westerlies domain. Quasi-periods of 7-8 years were common and were detected in all domains. The high-frequency variability mode (2-6 years quasi-periods) was universal in all domains. Moreover, 2.5-year quasi-periods were detected at the greatest number of meteorological stations (41%), followed by 3-year quasi-periods (i.e., 2.9 years, 29%) and 5-year quasi-periods (i.e., 4.9 years, 19%), which were widely detected in all domains. Base periodicity or two-base periodicity covered most of the periods. Of all the periodicities detected, base periods (i.e., 2- and 3-year quasi-periods) accounted for about 53% of all periods, while quasi-periods of 6 years or less accounted for 97%.

In the Third Pole region, significant quasi-periods of P can be detected several times, once, or not at all (Figure 4a). Quasi-periods were widely detected at all meteorological stations, with almost all (95%) consisting of either one or two periods. Only ~3% of the meteorological stations had three or more quasi-periods, which were all distributed in the western westerlies domain. Among these, one meteorological station with records going back 95 years had four quasi-periods. Nine of the meteorological stations had no quasi-periods due to their short records, which limited detection.

To investigate the timing of periodicity, this study used 215 meteorological stations with P data covering the period 1961-1990. Of these meteorological stations, 212 showed significant quasi-periods. Figure 4b shows the temporal coverage of quasi-periods from 1961-1990. Quasi-periods were detected over most (84%) of the 30-year study period, indicating significant quasi periodicity in the Third Pole P. Quantifying the periodicity is useful for understanding changes in P and aiding future predictions.

For the four climate domains, wavelet analysis showed that the power spectrum of P was dominated by statistically significant high frequency oscillations (i.e., with short periods, Figure 5). None of the P time series exhibited low frequency distributions, except for a statistically insignificant peak at 8 years that was detected in the transitional domain. Thus, the P time series were essentially absent from statistically significant long-term periodic components. The dominant statistically significant oscillations were on scales of <5 years, where a 3-year period (with a spectral peak at 2.9 years) was detected in the westerlies and Indian monsoon domains, and a 4-year period (with a spectral peak at 4.1 years) was detected in the transitional domain.

It is widely recognised that large-scale circulations have a strong influence on the variability of P, causing quasi-periodic characteristics and transitional behaviour in P time series. To understand the relationships between circulations and P, this study examined SAWS over a range of quasi-periods from 2 to 8 years for P and large-scale circulation indices that were significantly (p<0.05) correlated with P (as shown in Table 1 and Figure 6). The AO and IDO had less influence on the variability of P, as they showed a non-significant relationship with P in all domains. The WJ and the ENSO were two major indices that are significantly correlated with P variability in most of the Third Pole. The SAM significantly affected P variability only in Indian monsoon domain. In the western westerlies domain, NAO and ENSO showed similar variability to the P data, especially when the variance was high from 1961-1980 (Figure 6a). Unexpectedly, the WJ did not show a significant relationship with P, although it transports an important amount of water vapour, exhibiting that the effect of WJ on P varibility was relatively stable. The NAO modulated multidecadal changes in North Atlantic sea temperature and ENSO, and drove the cross-basin interactions between the Atlantic and Pacific Oceans. The Atlantic multidecadal oscillation influenced the WJ by modulating the 500hPa wave train associated with ENSO. A 3-year quasi-period was found for the NAO, ENSO, and P.

In the eastern westerlies domain, the role of the EASM and the WJ was significant in addition to the ENSO. However, the variance in the variability of the WJ showed an inverse trend with P (Figure 6b). Although the WJ transports most of the vapour, the EASM, which blocked by the western boundary of the Qaidam Basin, brought vapour here and together with the WJ influenced P variability. The EASM acted in the Yangtze and Huaihe basins when El Niño was strong, moving into the eastern westerlies domain to influence P variability. Their direct and indirect influences made P more stable after early 1970s. Similar to the western westerlies domain, 3-year quasi-periods were also detected in the circulation indices and P data. In the transitional domain, there was a close correlation between the EASM, WJ, and ENSO and P variability, similar to the findings from the eastern westerlies domain. However, in contrast to the other three domains, the variance in the transitional domain all generally increased during 1961-1990 (Figure 6c). The combined influence of the three indices made P more variable. P showed quasi-periods from 2 to 4 years, covering the 3-year quasi-periods of the EASM, WJ, and ENSO. In the Indian monsoon domain, the variance in the variability showed that P was mainly influenced by the WJ, SAM, and NAO (Figure 6d). This is the only domain where the SAM had a significant influence on the P variability. Similar to the eastern westerlies domain, the variance in the variation of the WJ showed the opposite pattern to P. The significant P variance in the first half of the period is a complex result of the interaction between the SAM and the WJ. Except for the SAM, both the WJ and the NAO corresponded to a 3-year quasi-period with P.

Our results verified that increasing trends of P were detected in most of the studied stations (about 60% of the meteorological stations), while showing high variation in spatial pattern (Figure 2) (e.g., Yang et al., 2011; Ren et al. 2017; Wang et al., 2018). Influenced by complex circulation systems (e.g., Indian monsoon, Westerlies, and local recycling) across the TP, two moisture tracks from the west and south dominate the P (Yao et al., 2012; Curio et al., 2015). The decreasing trends of P detected in the southeast (shown in Figure 2b), which is mainly controlled by the water vapour from the Bay of Bangla (Wang et al., 2017; Wang et al., 2018; Ma et al., 2020), while the P in the western and northern TP and the central TP, which is mainly dominated by moisture, comes from the west (about 70%) and the west and south (about 40%), where unexpected bias may exist due to local recycling), respectively (Ma et al., 2020). Wind calming may have weakened water vapour exchange between the Asia monsoon region and the TP, resulting in less P in the monsoon-impacted southern and eastern TP (Yang et al., 2014).

In Central Asia, the ENSO phenomenon has been shown to be closely correlated with the occurrence of droughts (Barlow et al., 2002; Kumar and Hoerling, 2003). The NAO is closely associated with the Siberian High and plays an important role in influencing the climate of Central Asia (Wu and Wang, 2002; Duan and Wu, 2005) and the P periodicity in the Third Pole (Guo et al., 2018). The IOD can influence the Asian monsoon by modulating the South Asian high, and the western North Pacific anticyclone (Li and Mu, 2001), e.g., the positive IOD phase can induce excess precipitation by driving a Gill-type response with an anticyclonic anomaly over the Indian subcontinent and the Bay of Bengal, and then anomalous westerlies induce a shallow trough, and the associated anomalous southwesterlies transport moisture to the southeastern TP (Zhang and Duan, 2023). The WJ is closely correlated with the early spring (i.e., February, March, and April) sea surface temperature anomaly (SSTA) tripole pattern over the North Atlantic (Cui et al., 2015), which transports water vapour to the Third Pole through both its northern and southern boundaries, due to the barrier effect of the TP. The amount of water vapour transported by the WJ is smaller than that transported by the EASM and SAM. However, the WJ provides water vapour for most of the year and is the main large-scale circulation controlling winter vapour transport (Duan et al., 2015; Zhang et al., 2017a). Summer monsoons (i.e., the EASM and SAM), which are remotely influenced by the tropical region of the mid-eastern Pacific Ocean (as determined by the influence of ENSO) and the tropical southeastern and western Indian Ocean (as determined by the influence of IOD), can transport a large amount of water vapour from both the Indian Ocean and the Pacific Ocean to the Third Pole (Zhang et al., 2017a). In addition, a long term annual P record reconstructed from fossil pollen assemblages from the annually laminated sediments of a lake in the interior of the TP revealed that TP P is modulated by periodically coupled westerlies and the Asian monsoon, suggesting that the current high P in the interior of the TP may persist for the next few decades (Cui et al., 2020).

Chang et al. (2019) also reported that the EASM, the SAM, and the ENSO all affect P across the TP, as demonstrated by wavelet coherence on an interannual scale. Furthermore, studies about the WJ conducted along the western region of the TP observed significant interannual and decadal variability, which closely correlated with the intensity of the East Asian summer monsoon and summer rainfall in the eastern region of the Third Pole (Huang and Zhou, 2004). Most meteorological stations used in this study are situated within the eastern region of the Third Pole, which is susceptible to interactions between the WJ and Asian summer monsoons. These interactions make understanding the driving mechanisms of P periodicity complex. Overall, P variation modes from different domains, divided by the δ¹⁸O values of P (as reported by Yao et al., 2013), are influenced by interactions between large-scale circulations rather than solely by the dominant large-scale circulation that is the main regional water vapor source.

Changes in P trends in combination with evapotranspiration (ET) will inevitably alter the streamflow in a warming TP. As reported by Yang et al. (2014), P shows increasing trends in the central TP and decreasing trends in the TP periphery while ET shows increasing trends overall, resulting in decreased streamflow in the major water resource areas of the TP (semi- humid and humid zones in the eastern and southern TP) (Yang et al., 2014). While Wang et al. (2021) found that significant increases in both P (2.59 mm yr^-1) and ET (1.76 mm yr^-1; partly caused by the vegetation greening) have led to an insignificant increase in discharge (0.39 mm yr^-1) over the past 38 years in the upper Yangtze River basin from 1981 to 2018. In addition, evidence from station measurements and the Terrestrial Water Storage observations (estimated by the Gravity Recovery and Climate Experiment (GRACE) and other satellites) has also shown that increased P has played a dominant role in the increase in riving water storage, and consequently driven lake expansion in the TP (e.g., Zhang et al., 2017b, 2019; Lei et al., 2021). Indeed, the combination of snow melt, glacier melt and frozen ground degradation has led to a complex response of streamflow under warming climate. Unexpectedly, snow and glacier melt contributes a quarter of the total streamflow on average, but there is no prominent trend in the melting, while the frozen ground degradation has little influence on the streamflow trend in the recent decades (Xu et al., 2021).

Widespread advancement of the onset of the plant growing season (SOS) across the TP has been reported (e.g., Shen et al., 2015b; Piao et al., 2019). The causes have been explained as increasing preseason P combined with warming climate changes altering the SOS and the end of the plant growing season (EOS) across the TP (Shen et al., 2015a, 2015b). That is, increased preseason P could directly advance the SOS, and regulate the SOS response to forcing temperature in more drier areas and wetter areas, respectively (Shen et al., 2015a). Conversely, vegetation change may regulate the hydrological processes. For example, in the simulation by Li et al. (2018), the forest cover increased significantly, P did not change in the simulation, while ET increased, leading to a slight decrease in soil moisture. In addition, changes in snowfall fraction and snow melt with warming climate change will affect vegetation, and even resulted in asymmetric hydrological effects in wet and dry years (Li et al., 2021). In fact, the uneven response of vegetation to an early onset of the thermal growing season will inevitably induce the complicated hydrological effects (e.g, enhancing tree growth in cold wet areas rather than in dry areas, Gao et al., 2022). Under a warming climate, alteration in P, and its interactions with vegetation, soil and glaciers will inevitably introduce large uncertainties into the hydrological system, which should be explored in further studies.

In this study, the Morlet wavelet transform was used to investigate the interannual periodicity of annual P time series in the Third Pole, and to explore the associated driving mechanisms. Data from meteorological stations showed significant quasi-periods ranging from 2 to 11 years. The distribution of the high-frequency variability mode (with a quasi-period of 2 to 6 years) was universal, while the low-frequency variability mode (with a quasi-period of 7 to 11 years) occurred only in the westerlies and Indian monsoon domains. Base periodicity and two-base periodicity comprised the majority of these periods, and quasi-periods of 6 years or less accounted for 97% of all detected periodicity. Spatially, periodicity was widespread, with almost all meteorological stations (95%) showing evidence of either one or two periods. Temporally, from the 215 meteorological stations with P data over 1961-1990, significant periodicity was observed in 84% of the 30-year study period. At the climate domain scale, statistically significant high-frequency oscillations (i.e., short periods of <5 years) dominated the power spectra of P.

The large-scale circulations that strongly influenced the periodic variation of P differed between domains. Furthermore, the influence of the same large-scale circulation on P differed or even reversed between domains. In addition, the mode of P variability in the different domains was influenced by the interactions between the large-scale circulations and not only by the dominant large-scale circulation controlling water vapour transport.

Overall, this study has comprehensively analysed the P periodicity across the Third Pole, and explored the associated mechanisms with respect to the large-scale circulations. The results of this study improve our understanding of P variability, not just the amount of P, with important implications for hydrological science and water resource management.