Influence of August Asian–Pacific Oscillation on September Precipitation in Northern Xinjiang

Abstract

For arid and semi-arid regions like Xinjiang, analyzing the spatiotemporal patterns of September precipitation and their atmospheric circulation teleconnections is crucial for ecosystem preservation. This research examined how the August Asian–Pacific Oscillation (APO) influenced the September precipitation patterns in northern Xinjiang. The results show that the thermal anomalies resulting from the August APO exhibited persistence into September, triggering atmospheric circulation anomalies that ultimately affected the precipitation patterns in northern Xinjiang, with a notable negative correlation. The positive (negative) August APO phase corresponded to reduced (increased) mid-tropospheric geopotential heights over Asia and the Arabian Sea, significantly enhancing anomalous cyclonic (anticyclonic) circulation patterns in these regions. These circulation patterns induced anomalous northerlies (southerlies) over northern Xinjiang and the region from eastern Iran to the Persian Gulf, thereby reducing (increasing) the moisture transport from the Arabian Sea. Furthermore, the anomalous divergence (convergence) of cold/warm air masses and subsidence (ascent) motions exacerbated (enhanced) these effects, ultimately suppressing (enhancing) the September precipitation in northern Xinjiang.

1. Introduction

Situated in the core zone of Central Asia’s arid belt, Xinjiang is characterized by a fragile ecosystem and limited precipitation [1,2], receiving fewer than 160 mm of precipitation per year on average [3]. Previous studies have demonstrated that anomalies in the autumn precipitation in Xinjiang, particularly in September, have significant implications for crop cultivation, socioeconomic progress, and routine activities [4,5,6,7]. Therefore, understanding the mechanisms driving the September precipitation variability in Xinjiang is crucial for improving seasonal climate forecasting and water resource management.

The autumn precipitation in Xinjiang exhibits notable variability and is directly influenced by the mid-latitude westerly circulation [8,9,10]. Synoptic-scale systems embedded within this circulation, such as the Central Asian trough and Central Asian vortex [11,12], can directly induce precipitation through dynamic lifting and frontal processes. Additionally, certain high-latitude climatic factors, including the Northern Hemisphere polar vortex and the Atlantic–European polar vortex, may indirectly influence the autumn precipitation in Xinjiang by modulating the southward intrusion of cold air and the intensity of westerly wave fluctuations [13]. Moreover, the autumn precipitation in Xinjiang is also remotely modulated by teleconnection patterns, such as the ENSO, AO, and NAO [14,15,16,17,18]. While earlier studies suggested that these factors can partially explain the mechanisms behind autumn precipitation anomalies in Xinjiang, the fundamental causes of such variability remain unclear due to the complex interactions of influencing factors across broad temporal and spatial scales. Therefore, to gain a more accurate and in-depth understanding of the mechanisms driving the September precipitation variability in Xinjiang, it is necessary to explore additional possible influencing factors.

The APO is a tropospheric thermal “seesaw” pattern that emerges during the summer, characterized by a zonal dipole in the upper-tropospheric eddy temperature across the Northern Hemisphere. This pattern manifests as an increase (decrease) in the eddy temperature over Asia accompanied by a corresponding decrease (increase) over the mid-latitude North Pacific [19]. The APO modulates atmospheric circulation, thereby influencing East Asia’s precipitation [20,21,22,23] and cyclone frequency [24] and the North Pacific sea surface temperature (SST) [25]. Existing research has highlighted the APO’s pivotal role in modulating East Asian precipitation and its significant impacts on the global climate variability. Recent research has begun to investigate the cross-seasonal effects of the summer APO on precipitation, although most studies have focused on eastern China [26,27]. The linkage connecting the earlier APO to the September precipitation in northern Xinjiang, however, remains understudied. While previous studies have identified a correlation between the APO and ENSO variability [28], this does not necessarily imply that Pacific SST anomalies primarily drive APO-related teleconnections. Therefore, the present study aims to clarify the potential mechanisms by which the preceding APO influences the September precipitation in northern Xinjiang, independent of ENSO effects.

This study is described progressively in three segments. Section 2 details the data, methods, and model employed in this research and is followed by Section 3 demonstrating the results. Finally, Section 4 offers the conclusions and a discussion.

2. Data, Methods, and Model

2.1. Data

The atmospheric reanalysis data employed in this research were sourced from the NCEP/NCAR reanalysis dataset [29]. The meteorological variables encompassed the geopotential height (H), air temperature (T), u-wind (U), v-wind (V), specific humidity (q), and omega (ω). Specifically, measurements for variables H, T, U, V, and ω were available at 17 vertical levels, while measurements for q were available at 8 levels. The dataset had a grid spacing of 2.5° × 2.5°.

Precipitation data were acquired from the CN05.1 daily precipitation dataset, developed by Wu and Gao [30]. This dataset was constructed by interpolating observations from over 2400 ground meteorological stations across China, with a grid spacing of 0.25° × 0.25°.

The September Niño 3.4 Index (SNI), developed and maintained by the NOAA Climate Prediction Center, served as a key metric of the influence of the ENSO’s externally forcing on the climate conditions.

The analysis period spanned from 1961 to 2020.

2.2. Methods

The APO refers to a teleconnection pattern characterized by a “seesaw” structure in the eddy temperatures across the upper and middle troposphere over Asia and the North Pacific. Following Zhao’s definition [19], the eddy temperature is defined as $T^{\prime }=T-\overline{T}$, where $T$ is defined as the vertical mean of the air temperature over the 500–200 hPa pressure levels, and $\overline{T}$ denotes the zonal mean of $T$. Consistent with previous studies, the APO index (APOI) is defined as $\mathrm{APO}\mathrm{index}=T_{{85}^{\circ }-{135}^{\circ }\mathrm{E},{35}^{\circ }-{55}^{\circ }\mathrm{N}}^{\prime }-{T^{\prime }}_{{160}^{\circ }\mathrm{E}-{130}^{\circ }\mathrm{W},{20}^{\circ }-{45}^{\circ }\mathrm{N}}$.

To isolate the ENSO’s influence, we employed conditional maximum covariance analysis to extract the component of the preceding APO that affected the September precipitation, independently of the ENSO [31]. The computational approach was based on the following equation:

(1)$\xi ={\xi }^{\ast }-SNI\times \mathit{cov}(\xi ,SNI)/\mathit{var}(SNI)$

where cov represents the covariance linking the chronological series of initial meteorological variables, var denotes the SNI’s variance, and $\xi$ describes the meteorological variable component exhibiting no linear dependence on the SNI.

Prior to all diagnostic analyses, long-term linear trends were removed from all the datasets using linear regression to focus on the interannual variability.

In this study, we further employed Empirical Orthogonal Function (EOF) analysis, regression analysis, and numerical modeling to explore the influence of the August APO on the September precipitation in northern Xinjiang. The statistical significance of the correlation analysis was evaluated by applying the Student t-test.

2.3. Model

The numerical modeling in this research was conducted using the linear baroclinic model (LBM), which was developed by the University of Tokyo [32]. Based on previous studies [33,34], we have found that the atmospheric LBM is widely used to validate the response of the atmospheric circulation to diabatic heating/cooling. This model is based on a linearized version of the time-integrated primitive equations and can serve as a useful tool for analyzing the influence of anomalous diabatic heating on the atmospheric circulation. Notably, it has been extensively applied in simulating heat sources associated with the APO and the Tibetan Plateau [35,36].

3. Results

3.1. Climatology Pattern of August APO

Based on a previous study [19], an EOF decomposition was conducted on the August $T^{\prime }$ over 0–60° N from 1961 to 2020. The leading EOF mode of the upper-tropospheric $T^{\prime }$ (explaining 18.8% of the total variance) revealed a nearly hemisphere-wide seesaw pattern (Figure 1a), with positive $T^{\prime }$ anomalies over Asia (85–135° E, 35–55° N) and negative $T^{\prime }$ anomalies over the central–eastern Pacific (160° E–130° W, 20–45° N).

Figure 1b shows the zonal–vertical profile of the climatological August $T^{\prime }$ averaged over 30–50° N. The vertical structure clearly exhibits the APO pattern in the mid-to-upper tropospheric levels, revealing an antiphase of $T^{\prime }$ linking Asia with the central–eastern Pacific.

3.2. Linkage of August APO to September Precipitation in Northern Xinjiang on Interannual Timescales

Figure 2a illustrates the geographical pattern of the correlations between the August APOI and September precipitation across northern Xinjiang from 1961 to 2020. As shown in the figure, a significant negative correlation region (with a central value of below −0.49) was evident across most of northern Xinjiang (43–50° N, 82–94° E), which suggests that less (more) September precipitation in northern Xinjiang is likely associated with a stronger (weaker) August APO. Furthermore, even after removing the influence of the ENSO (Figure 2b), the negative correlation pattern over northern Xinjiang remained robust. Therefore, we directed our attention to analyzing the August APO’s linkage with the September precipitation in northern Xinjiang, independent of the ENSO variability. The results showed similar patterns based on EAR5 with NCEP/NCAR reanalysis.

The September precipitation over northern Xinjiang, averaged within the region (43–50° N, 82–94° E), was defined using the Northern Xinjiang Precipitation Index (NXPI). We employed this index to analyze the linkage between the August APO and September precipitation across northern Xinjiang. To further investigate this relationship, we calculated the standardized APOI and NXPI for the period from 1961 to 2020 (Figure 3a), which exhibited a significant negative correlation, with a correlation coefficient of −0.415, statistically significant at the 99% confidence level. After removing potential autocorrelations, the correlation remained robust at −0.406. Furthermore, using a threshold of one standard deviation, we defined extreme September precipitation occurrences in northern Xinjiang and examined their linkage to the August APO. During the analysis period, we identified that there were 22 extreme precipitation years in northern Xinjiang, with the NXPI and APOI showing a 77% opposite-phase rate. For the remaining 38 normal years, this anti-correlation persisted at 74%, indicating that the August APO consistently shows predictive potential for both extreme and non-extreme September precipitation regimes in northern Xinjiang.

To examine potential non-stationarity in the APO–precipitation relationship during the 60-year study period, we calculated sliding correlations between the APOI and NXPI using 21- and 25-year sliding windows (Figure 3b). The results indicate that the September precipitation and the August APO generally maintained a stable negative correlation after the 1990s. We performed regression analyses of 850 hPa winds and vertically integrated the moisture flux during 1961–1990 and 1991–2020. The results suggest that this may have been associated with weakened anomalous southerlies over the eastern China monsoon region.

3.3. Atmospheric Circulation Anomalies Associated with NXPI and APOI

To further validate the relationship between the NXPI and APOI, we investigated the atmospheric circulation anomalies induced by the August APO. The regression of 850 hPa wind fields onto the NXPI (Figure 4a) revealed that anomalous southwesterly winds over northern Xinjiang transport low-latitude moisture from Central Asia into the region, resulting in increased precipitation during September. In contrast, during positive APO phases, northern Xinjiang is predominantly influenced by anomalous northerlies (Figure 4b). Additionally, the anomalous northerlies extending from eastern Iran to the Persian Gulf region substantially reduce the moisture influx originating from the Arabian Sea.

As shown in Figure 5a, an increase (decrease) in the September precipitation over northern Xinjiang is closely associated with anomalous upward (downward) motion and prevailing anomalous westerly (easterly) winds over the region. Conversely, northern Xinjiang exhibits subsidence during the positive phase of the APO, effectively suppressing convective activity (Figure 5b).

Notably, the precipitation variability over northern Xinjiang is primarily regulated by moisture flux convergence or divergence induced by anomalous atmospheric circulation. Cyclonic circulation centered over the West Siberian Plain facilitates the transport of moisture from Central Asia into northern Xinjiang, where significant moisture flux convergence is observed, leading to increased precipitation (Figure 6a). Regression of the vertically integrated moisture flux onto the APOI indicated that during intensified APO phases, the moisture transport from the Arabian Sea to northern Xinjiang is significantly reduced. Meanwhile, the region exhibits anomalous moisture flux divergence, which suppresses precipitation (Figure 6b).

The above analysis indicates that the August APO influences the September precipitation over northern Xinjiang through modulation of the lower-level wind field and exhibits a robust correlation with the precipitation variability in this sector. Further analysis of the 500 hPa geopotential height revealed that an intensified (weakened) trough over southern Siberia and an enhanced (weakened) ridge over southeastern China are likely contributors to the increase (decrease) in the September precipitation over northern Xinjiang (Figure 7a). Moreover, the regression of the 500 hPa geopotential height onto the APOI indicated that during intensified (weakened) APO phases, the troughs over Asia and the Arabian Sea tend to deepen (weaken). The cyclonic circulation associated with these intensified troughs leads to anomalous northerlies on their western flank, which suppress the transport of moisture from the low and mid-latitudes into northern Xinjiang, thereby reducing the precipitation in this sector.

3.4. Possible Physical Mechanisms

These findings suggest that the August APO is a pivotal forecasting indicator for the September precipitation in northern Xinjiang due to its delayed influence on the atmospheric circulation patterns. Zhao et al. pointed out that heating anomalies over the Asian landmass can induce significant changes in the tropospheric temperature across the region [28]. As a key component of the APO, the persistence of these changes in the tropospheric temperature can contribute to the persistence of the APO pattern. This generates further questions concerning the specific atmospheric circulation mechanisms that connect the August APO with the September precipitation in northern Xinjiang. To understand this process, we analyzed the $T^{\prime }$ in September in relation to the APOI and NXPI (Figure 8). The correlation between $T^{\prime }$ and the APOI revealed a clear APO-like pattern (Figure 8a), indicating that thermal anomalies over the Asian–Pacific region, which exhibit key features consistent with those of the APO, can last from August into September and then induce corresponding atmospheric circulation anomalies. Meanwhile, the correlation between $T^{\prime }$ and the NXPI demonstrated an out-of-phase seesaw oscillation pattern linking Asia with the North Pacific (Figure 8b), suggesting that the earlier dipole-like thermal differences in the upper troposphere over the Asian–Pacific region may influence the September precipitation in northern Xinjiang.

3.5. Numerical Climate Simulations

To validate the atmospheric circulation mechanisms triggered by the APO, we designed LBM numerical simulation experiments. An elliptical thermal forcing characterized by a Gamma function vertical profile (dilation parameter = 20) was imposed, with its horizontal center positioned over the western Tibetan Plateau (32.5° N, 80° E) (Figure 9a). Based on previous studies [35,36], we adopted an intermediate heating intensity of 2 K per day near 400 hPa (σ = 0.4), simulating upper–middle tropospheric heating over the Tibetan Plateau (Figure 9b). Using the mean August–September climatological conditions as the basic state, the model integration spanned 30 days, with the analysis based on the last response averaged over 15 days from the final simulation period.

The response of the 850 hPa wind, as shown in Figure 9c, revealed that the thermal anomaly induced by the August APO triggers lower-tropospheric northerly responses over northern Xinjiang. Simultaneously, the anomalous northerlies extending from eastern Iran to the Persian Gulf region substantially diminish the moisture influx originating from the Arabian Sea. Zonal wind responses averaged over 45–50° N (Figure 9d) highlighted pronounced mid-to-lower-tropospheric subsidence (300–700 hPa) between 82 and 90° E. The wind field response aligns well with the regression-derived patterns shown in Figure 4b and Figure 5b, proving that the thermal anomaly caused by the August APO can persist into September and influence the September precipitation in northern Xinjiang through the atmospheric circulation.

4. Conclusions

Using reanalysis data and numerical climate simulations, this study examined how the August APO influences the September precipitation patterns in northern Xinjiang and its associated physical mechanisms (1961–2020).

The September precipitation in northern Xinjiang exhibited a significant negative correlation with the August APO index, with a coefficient of −0.41 that reached significance at the 99% confidence level, confirming their inverse temporal relationship. Additional investigations demonstrated that atmospheric circulation anomalies in September associated with the August APO led to a reduction in the September precipitation in northern Xinjiang. We further examined the intrinsic mechanism linking the August APO to the September precipitation in northern Xinjiang. The thermal anomalies induced by the APO in the mid- and upper troposphere, persisting into September, are capable of influencing atmospheric circulation anomalies during that season. Furthermore, the August APO anomalies can influence the geopotential height distributions across the middle and low troposphere, with the resulting wind anomalies leading to notable changes in the precipitation patterns across northern Xinjiang. During positive (negative) phases of the August APO, the 500 hPa East Asian trough intensifies (weakens), inducing anomalous lower-tropospheric northerlies (southerlies) over northern Xinjiang, accompanied by anomalous divergence (convergence) of cold/warm air masses and subsidence (ascent) motions, thereby reducing (increasing) the precipitation in September. Additionally, the 500 hPa trough over the Arabian Sea also intensifies (weakens), and the cyclonic (anticyclonic) circulations induced by these troughs (ridges) result in anomalous northerlies (southerlies) from eastern Iran to the Persian Gulf region, reducing (enhancing) the moisture transport from the Arabian Sea.

Author Contributions

Conceptualization, W.H.; data curation, Y.Z.; formal analysis, Y.Z.; methodology, Y.Z.; project administration, W.H.; resources, W.H.; software, Y.Z.; validation, Y.Z.; visualization, Y.Z.; writing—original draft, Y.Z.; writing—review and editing, W.H. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors would like to acknowledge Yunxiao Li of the Xianyang Meteorological Bureau for her helpful suggestions.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures

Figure 1 (a) The first EOF mode and (b) zonal–vertical profile of the mean August $T^{\prime }$ (°C) over 30–50° N for 1961–2020. Shaded areas indicate the terrain elevation.

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Figure 2 (a) Geographical pattern of the interannual correlation coefficients between the August APOI and September precipitation across northwest China; (b) is the same as (a), but with the influence of the ENSO removed. Stippling indicates regions significant at the 95% confidence level.

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Figure 3 (a) Time series of the normalized APOI (red line) and NXPI (blue line) and (b) series of rolling correlations between the NXPI and APOI over 21-year (blue line) and 25-year (red line) rolling windows for 1961–2020. The dashed lines indicate significant results at the 95% confidence level.

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Figure 4 Regression of 850 hPa wind fields (vectors, unit:m·s⁻¹) onto (a) the NXPI and (b) the APOI for 1961–2020. Blue vectors indicate areas significant at the 95% confidence level, and shaded areas denote a terrain elevation exceeding 1500 m.

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Figure 5 Regressions of longitude–pressure section of zonal winds (vectors, unit: m·s⁻¹) averaged over 42.5–50° N onto (a) the NXPI and (b) the APOI for 1961–2020. Red (blue) vectors indicate areas significant at the 95% confidence level. Black area represents the terrain elevation.

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Figure 6 Regressions of vertically integrated moisture flux (vectors, unit: kg·m⁻¹·s⁻¹) and vertically integrated moisture flux divergence (shading, unit: 10⁻⁵·kg·m⁻²·s⁻¹) onto (a) NXPI and (b) APOI for 1961–2020. Stippling and blue vectors indicate areas significant at 95% confidence level.

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Figure 7 Regession of 500 hPa geopotential height (shading, unit: gpm) onto (a) NXPI and (b) APOI for 1961–2020. Stippling indicates areas significant at 95% confidence level.

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Figure 8 September $T^{\prime }$ correlations with (a) APOI and (b) NXPI at 500–200 hPa for 1961–2020. Stippling indicates areas significant at 95% confidence level.

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Figure 9 LBM simulations: (a) horizontal forcing at σ = 0.37, (b) vertical forcing at 32° N (shading, unit: K·day⁻¹; black area represents terrain), (c) response of 850 hPa wind field (vectors, unit: m·s⁻¹; white areas denote terrain elevation above 1500 m), and (d) longitude–pressure section of zonal wind averaged over 45–50° N (vectors, unit: m·s⁻¹; black area represents terrain).

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References

1. Yao, J.Q.; Chen, Y.N.; Zhao, Y.; Guan, X.F.; Mao, W.Y.; Yang, L.M. Climatic and associated atmospheric water cycle changes over the Xinjiang, China. J. Hydrol.; 2020; 585, 124823. [DOI: https://dx.doi.org/10.1016/j.jhydrol.2020.124823]

2. Yao, J.Q.; Li, M.Y.; Tuoliewubieke, D.; Chen, J.; Mao, W.Y. The assessment on “warming-wetting” trend in Xinjiang at multi-scale during 1961–2019. Arid. Zone Res.; 2022; 39, pp. 333-346.

3. Li, B.; Chen, Y.; Chen, Z.; Xiong, H.; Lian, L. Why does precipitation in northwest China show a significant increasing trend from 1960 to 2010?. Atmos. Res.; 2016; 167, pp. 275-284. [DOI: https://dx.doi.org/10.1016/j.atmosres.2015.08.017]

4. Hu, Q.; Pan, X.; Hu, Y.; Huang, B.; Han, Y.; Zhang, D. Impact of climate change on cotton growth and yield in Xinjiang, China. Field Crops Res.; 2022; 275, 108357.

5. Cheng, J.; Yin, S. Analysis of Drought Characteristics and Its Effects on Crop Yield in Xinjiang in Recent 60 Years. Sustainability; 2021; 13, 13833. [DOI: https://dx.doi.org/10.3390/su132413833]

6. Abudoukerimu, A.; Hu, S.; Maimaitireyimu, N. Effect of Climate Change on Cotton Growth Period and Yield in Kashgar City, Xinjiang Uygur Autonomous Region. Chin. J. Eco-Agric.; 2015; 23, pp. 919-930.

7. Li, M.; Yao, J.; Guan, J.; Zheng, J. Vegetation Browning Trends in Spring and Autumn over Xinjiang, China, during the Warming Hiatus. Remote Sens.; 2022; 14, 1298. [DOI: https://dx.doi.org/10.3390/rs14051298]

8. Aizen, E.M.; Aizen, V.B.; Melack, J.M.; Nakamura, T.; Ohta, T. Precipitation and atmospheric circulation patterns at mid-latitudes of Asia. Int. J. Climatol. J. R. Meteorol. Soc.; 2001; 21, pp. 535-556. [DOI: https://dx.doi.org/10.1002/joc.626]

9. Aizen, V.B.; Aizen, E.M.; Joswiak, D.R.; Fujita, K.; Takeuchi, N.; Nikitin, S.A. Climatic and atmospheric circulation pattern variability from ice-core isotope/geochemistry records (Altai, Tien Shan and Tibet). Ann. Glaciol.; 2006; 43, pp. 49-60. [DOI: https://dx.doi.org/10.3189/172756406781812078]

10. Chen, F.; Chen, J.; Huang, W.; Chen, S.; Huang, X.; Jin, L.; Jia, J.; Zhang, X.; An, C.; Zhang, J. . Westerlies Asia and monsoonal Asia: Spatiotemporal differences in climate change and possible mechanisms on decadal to sub-orbital timescales. Earth-Sci. Rev.; 2019; 192, pp. 337-354. [DOI: https://dx.doi.org/10.1016/j.earscirev.2019.03.005]

11. Yang, L.; Zhang, Y.; Qin, H. Some advances and problems in the study of heavy rain in Xinjiang. Clim. Environ. Res.; 2011; 16, pp. 188-198.

12. Guo, N.; Zhou, Y.; Yang, L. Statistical analysis of Central Asian vortices and their influence on precipitation in Xinjiang. Atmospheric Res.; 2021; 249, 105327. [DOI: https://dx.doi.org/10.1016/j.atmosres.2020.105327]

13. Zhou, G.; Chen, Y.; Yao, J. Variations in precipitation and temperature in Xinjiang (Northwest China) and their connection to atmospheric circulation. Front. Environ. Sci.; 2023; 10, 1082713. [DOI: https://dx.doi.org/10.3389/fenvs.2022.1082713]

14. Huang, W.; Chen, F.; Feng, S.; Chen, J.; Zhang, X. Interannual precipitation variations in the mid-latitude Asia and their association with large-scale atmospheric circulation. Chin. Sci. Bull.; 2013; 58, pp. 3962-3968. [DOI: https://dx.doi.org/10.1007/s11434-013-5970-4]

15. Guan, X.; Sun, W.; Li, M. Climate change in north Xinjiang and its response to Arctic oscillation during the period of 1965–2012. Arid Zone Res.; 2016; 33, pp. 681-689.

16. Dai, X.; Wang, P.; Zhang, K. A study on precipitation trend and fluctuation mechanism in northwestern China over the past 60 years. Acta Phys. Sin.; 2013; 62, 129201. [DOI: https://dx.doi.org/10.7498/aps.62.129201]

17. Yang, R.; Xing, B. Teleconnections of Large-Scale Climate Patterns to Regional Drought in Mid-Latitudes: A Case Study in Xinjiang, China. Atmosphere; 2022; 13, 230. [DOI: https://dx.doi.org/10.3390/atmos13020230]

18. Chen, X.; Wang, S.; Hu, Z.; Zhou, Q.; Hu, Q. Spatiotemporal characteristics of seasonal precipitation and their relationships with ENSO in Central Asia during 1901–2013. J. Geogr. Sci.; 2018; 28, pp. 1341-1368. [DOI: https://dx.doi.org/10.1007/s11442-018-1529-2]

19. Zhao, P.; Zhu, Y.; Zhang, R. An Asian-Pacific Teleconnection in Summer Tropospheric Temperature and Associated Asian Climate Variability. Clim. Dyn.; 2007; 29, pp. 293-303. [DOI: https://dx.doi.org/10.1007/s00382-007-0236-y]

20. Zhao, P.; Wang, B.; Zhou, X. Boreal summer continental monsoon rainfall and hydroclimate anomalies associated with the Asian-Pacific Oscillation. Clim. Dyn.; 2012; 39, pp. 1197-1207. [DOI: https://dx.doi.org/10.1007/s00382-012-1348-6]

21. Zhou, X.; Zhao, P.; Liu, G. Asian-Pacific Oscillation index and variation of East Asian summer monsoon over the past millennium. Chin. Sci. Bull.; 2009; 54, pp. 3768-3771. [DOI: https://dx.doi.org/10.1007/s11434-009-0619-z]

22. Zhou, B.; Zhao, P. Influence of the Asian-Pacific oscillation on spring precipitation over central eastern China. Adv. Atmos. Sci.; 2010; 27, pp. 575-582. [DOI: https://dx.doi.org/10.1007/s00376-009-9058-7]

23. Liu, G.; Zhao, P.; Dong, C. The relationship between the Asian-Pacific oscillation and January precipitation anomalies over southern China. Acta Meteorol. Sin.; 2013; 71, pp. 462-475.

24. Zhou, B.; Cui, X.; Zhao, P. Relationship between the Asian-Pacific oscillation and the tropical cyclone frequency in the western North Pacific. Sci. China Ser. D-Earth Sci.; 2008; 51, pp. 380-385. [DOI: https://dx.doi.org/10.1007/s11430-008-0014-7]

25. Zhou, B.; Zhao, P.; Cui, X. Linkage between the Asian-Pacific Oscillation and the sea surface temperature in the North Pacific. Chin. Sci. Bull.; 2010; 55, pp. 1193-1198. [DOI: https://dx.doi.org/10.1007/s11434-009-0386-x]

26. Xia, C.; Hua, W.; Hu, Q.; Yang, K.; Ming, J.; Wu, X.; Fan, G. Linkage Between Preceding August Asian–Pacific Oscillation and September Precipitation Over Southeast China. Front. Earth Sci.; 2022; 10, 917445. [DOI: https://dx.doi.org/10.3389/feart.2022.917445]

27. Lin, Z.; Zhu, J.; Hua, W.; Fan, G. Impact of the August Asian-Pacific Oscillation on Autumn Precipitation in Central Eastern China. Asia-Pacific J. Atmos. Sci.; 2021; 57, pp. 181-190. [DOI: https://dx.doi.org/10.1007/s13143-020-00187-1]

28. Zhao, P.; Yang, S.; Jian, M.; Chen, J. Relative Controls of Asian–Pacific Summer Climate by Asian Land and Tropical–North Pacific Sea Surface Temperature. J. Clim.; 2011; 24, pp. 4165-4188. [DOI: https://dx.doi.org/10.1175/2011JCLI3915.1]

29. Kalnay, E.; Kanamitsu, M.; Kistler, R.; Collins, W.; Deaven, D.; Gandin, L.; Iredell, M.; Saha, S.; White, G.; Woollen, J. . The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc.; 1996; 77, pp. 437-472. [DOI: https://dx.doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2]

30. Wu, J.; Gao, X.-J. A gridded daily observation dataset over China region and comparison with the other datasets. Chin. J. Geophys.; 2013; 56, pp. 1102-1111. (In Chinese)

31. An, S.-I. Conditional Maximum Covariance Analysis and its Application to the Tropical Indian Ocean SST and Surface Wind Stress Anomalies. J. Clim.; 2003; 16, pp. 2932-2938. [DOI: https://dx.doi.org/10.1175/1520-0442(2003)016<2932:CMCAAI>2.0.CO;2]

32. Watanabe, M.; Kimoto, M. A two-layer atmospheric general circulation model with gray radiation: Sensitivity studies on the dynamics and thermodynamics of the model response to SST anomalies. J. Meteorol. Soc. Jpn.; 1999; 77, pp. 721-736.

33. Watanabe, M.; Kimoto, M. Atmospheric wave dynamics simulated by a linear baroclinic model with a realistic basic state. J. Clim.; 2000; 57, pp. 2209-2225.

34. Hu, P.; Chen, W.; Chen, S.; Yang, R.; Wang, L.; Liu, Y. Revisiting the linkage between the Pacific-Japan pattern and Indian summer monsoon rainfall: The crucial role of the Maritime Continent. Geophys. Res. Lett.; 2024; 51, e2023GL106982. [DOI: https://dx.doi.org/10.1029/2023GL106982]

35. Li, Y.; Hua, W.; Luo, F.; Zhu, L. Interannual relationship between the Asian–Pacific Oscillation and summer sea surface temperature in the North Atlantic. Environ. Res. Lett.; 2024; 19, 014060. [DOI: https://dx.doi.org/10.1088/1748-9326/ad16a6]

36. Han, X.; Zhao, P.; Ye, J.; Li, J. The impact of warming Tibetan Plateau on the 2020 summer unprecedented Northeastern Pacific Marine heatwave. NPJ Clim. Atmos. Sci.; 2025; 8, 98. [DOI: https://dx.doi.org/10.1038/s41612-025-00979-0]