1. Introduction

Given the high demand for water supply in the populous countries of South and East Asia, the variability and prediction of summer rainfall in these regions has long been of great concern. Previous studies have led to important progress in understanding the factors and processes involved in the year-to-year summer rainfall variations of South and East Asia (e.g. Webster et al. 1998). One important factor is ENSO, which imposes a substantial impact on both Indian and East Asian summer rainfall variability (e.g. Walker 1923;Huang and Wu 1989; Wang, Wu, and Lau 2001; Wu, Hu, and Kirtman 2003).

The connection between the variations of Indian and East Asian summer rainfall is also a focus in the literature.Previous studies have revealed an in-phase relationship in the summer rainfall variations between India and North China (Guo and Wang 1988; Kripalani and Singh 1993;Zhang, Sumi, and Kimoto 1999; Kripalani and Kulkarni 2001; Wu 2002; Liu and Ding 2008; Greatbatch, Sun, and Yang 2013; Preethi et al. 2017a), and an out-of-phase relationship between India and southern Japan (Kripalani and Kulkarni 2001; Krishnan and Sugi 2001; Wang, Wu,and Lau 2001; Wu 2002; Yun, Lee, and Ha 2014) or South Korea (Kim et al. 2002; Preethi et al. 2017a). According to the literature, there are two pathways for the connection between Indian and East Asian summer rainfall variations,as summarized by Wu (2017). One pathway is atmospheric circulation change over the lower latitudes that modifies the moisture transport from the Indian Ocean to East Asia (Zhang, Sumi, and Kimoto 1999; Krishnan and Sugi 2001; Zhang 2001; Liu and Ding 2008). The other pathway is atmospheric circulation change over the midlatitudes of continental Asia, featuring a zonal wave pattern (Guo and Wang 1988; Kripalani, Kulkarni, and Singh 1997; Krishnan and Sugi 2001; Wang, Wu, and Lau 2001;Kim et al. 2002; Lu, Oh, and Kim 2002; Wu 2002; Enomoto,Hoskins, and Matsuda 2003). This zonal wave pattern is partly associated with anomalous Indian heating, and in turn modulates winds over East Asia (Wu 2002; Wu, Hu,and Kirtman 2003; Ding and Wang 2005; Liu and Ding 2008; Greatbatch, Sun, and Yang 2013).

A key issue is that the relationship between the variations of Indian and East Asian summer rainfall is not steady.Long-term changes in the relationship have been identified in previous studies (Guo 1992; Kripalani and Kulkarni 2001; Wu 2002; Wang and Huang 2006; Lin, Lu, and Wu 2017; Ha et al. 2017; Preethi et al. 2017b). One prominent change in the Indian–North China summer rainfall relationship occurred around the late 1970s (Wu 2002). The weakened connection after the late 1970s can be attributed to a shift in the distribution of large rainfall variability in India to lower latitudes, which weakened the impacts of anomalous Indian heating on the midlatitude Asian atmospheric circulation (Wu 2002; Wu and Wang 2002).The change in the relationship has also been detected in coupled climate model simulations (Preethi et al. 2017b;Wu and Jiao 2017). However, a plausible explanation for the non-stationarity in the above relationship has yet to be determined. The present study discusses various perspectives of the long-term changes in the connection between the summer rainfall variations of India and East Asia.

Following this introduction, we begin by showing the long-term changes in the Indian–East Asian summer rainfall relationship in observations and climate model simulations (Section 2). Then, we present different perspectives on what may contribute to the long-term changes in the relationship (Section 3). Lastly, some concluding remarks are provided (Section 4).

2. Long-term changes in the relationship

The long-term change in the relationship between Indian and North China summer rainfall variations in observations has been identified in previous studies. Guo (1992)pointed out that the Indian–North China summer rainfall correlation is weak during 1921–1950 but strong during 1891–1920 and 1951–1980. A change in the Indian–North China summer rainfall relationship around the late 1970s was noted by Kripalani and Kulkarni (2001), Wu (2002), Wu and Wang (2002), and Wang and Huang (2006). Long-term changes in the Indian–southern Japan and North China–southern Japan summer rainfall correlation were identified by Kripalani and Kulkarni (2001). The above changes in the Indian–East Asian summer rainfall relationship have been con firmed by Wu (2017) and Wu and Jiao (2017) using updated rainfall data. Secular changes have been observed in the relationship between the Indian and Yangtze–Huai River summer rainfall variations, as well as between the Indian and Korea–Japan summer rainfall variations (Ha et al. 2017; Preethi et al. 2017b).

Interdecadal changes in the relationships among Indian, North China, and southern Japan summer rainfall variations remain after ENSO-related signals have been removed (Wu 2017; Wu and Jiao 2017) – a point clearly demonstrated by comparing Figure 1(b) with Figure 1(a).In Figure 1(b), the impacts of the ENSO signal on the correlation have been removed through partial correlation with respect to JJAS Niño3.4 (5°S–5°N, 170°–120°W) SST anomalies. Figure 1 is similar to Figure 6 of Wu (2017),except for different rainfall data over land used in the correlation analysis. The Indian–southern Japan rainfall correlation around 1970 weakens after the ENSO signal is removed, indicative of a contribution from ENSO to the interdecadal change in the Indian–southern Japan rainfall correlation. Nevertheless, long-term changes are still present in the three correlations after removal of the ENSO signals. The results are consistent with Wu (2017) and Wu and Jiao (2017). This implies that long-term changes in the Indian–East Asian summer rainfall relationship may occur in the absence of ENSO impacts. In other words, long-term changes in the relationship may be induced by internal atmospheric variability.

Long-term changes in the Indian–North China summer rainfall relationship have been identified in the historical simulations of the CMIP5 climate models (Wu and Jiao 2017). Furthermore, there are also secular variations in the relationship between Indian and Korea–Japan summer rainfall variations in historical simulations and future projections of climate models (Preethi et al.2017b), albeit with the timing of the changes in the relationship differing from model to model and among different simulations of a single model (Wu and Jiao 2017).As an illustration, Figure 2 displays the 21-years running correlation between Indian and North China JJAS rainfall in two members of the CCSM4 and CNRM-CM5 simulations, which are selected from all the simulations shown in Wu and Jiao (2017). In the two members of the CCSM4 simulations, opposite correlation appears around 1980(Figure 2(a)). In the two members of the CNRM-CM5 simulations, the correlation is opposite in the 1980s but tends to vary in-phase before the 1960s (Figure 2(b)). The range of change in the correlation coefficient is large in the two members, from above +0.4 to below −0.4.

We conducted experiments with the barotropic model used by Lin, Lu, and Wu (2017). Anomalous heating was imposed over India and the prescribed mean winds were the average 200-hPa winds during the two periods (2040–2060 and 2012–2032) of the AGCM simulation. When the center of anomalous heating moves from 20°N to 25°N along 72.5°E, an obvious eastward shift in the wave-type response appears, regardless of whether the prescribed mean winds are based on the model years 2040–2060 or 2012–2032 ( figures not shown). This indicates that the atmospheric response is sensitive to the location of anomalous heating, which is consistent with Wu (2002) and Wu and Wang (2002). The response, however, does not show a large difference between the two prescribed mean winds( figures not shown).

3. Perspectives on the factors involved in the changes in the relationship

There may be many reasons behind the long-term changes in the Indian–East Asian summer rainfall relationship. Here,we discuss four of them.

Figure 1. (a) 21-years sliding correlation between area-mean Indian and northern China JJAS rainfall (red curve), between area-mean Indian and southern Japan JJAS rainfall (green curve), and between area-mean northern China and southern Japan JJAS rainfall (blue curve), based on GPCC version 7 0.5° gridded rainfall data (Schneider et al. 2015). (b) As in (a) except that the ENSO signal has been removed through partial correlation with respect to JJAS Niño3.4 (5°S–5°N, 170°–120°W) SST anomalies based on HadISST1.1 data(Rayner et al. 2003). Note: The horizontal dashed lines denote the 95% con fidence level of the correlation coefficient according to the Student’s t-test.

3.1. ENSO

Apparent long-term changes in the correlation are present in Figure 3(a). For example, the correlation coefficient between Indian and southern Japan summer rainfall varies from +0.4 to −0.4; and the correlation coefficient between North China and southern Japan summer rainfall varies from +0.4 to −0.8. The long-term change in the Indian–North China rainfall correlation is less obvious in the sliding correlation. The grid-point correlation in the two periods(model years 2040–2060 and 2012–2032) displays a clearer difference in North China (Figure 3(b) and (c)). The region of positive correlation in North China with Indian rainfall tends to be located more northwest in the model compared to observations (e.g. Wu 2017).

所谓的内部控制，就是依据具体的目标，对工作中可能出现的风险进行分析，依据可能出现的问题，采取特定的措施。工作的重点与核心就是风险管理，即对风险进行评估与应对。

The long-term changes in the Indian–East Asian summer rainfall relationship are illustrated in Figure 1(a), which shows three correlations in a 21-years sliding window using GPCC version 7 0.5° gridded rainfall data (Schneider et al. 2015). Following Wu (2017) and Wu and Jiao (2017), the domains for calculating the areamean Indian, North China, and southern Japan rainfall are (8°–28°N, 70°–86°E), (36°–42°N, 108°–118°E), and(31°–36°N, 130°–140°E), respectively. Apparently, the Indian–North China June–September (JJAS) rainfall correlation is higher during 1950s–1960s than during the 1980s and 1930s, which is consistent with previous studies (Kripalani and Kulkarni 2001; Wu 2002). The Indian–southern Japan and North China–southern Japan JJAS rainfall correlation is higher around 1970 than during the 1950s and 1990s. The results agree with Wu (2017)and Wu and Jiao (2017), whose studies used different rainfall data over land.

要当好一个秘书，与领导建立和谐融洽的关系非常重要，也是开展好工作的必要前提，每一个秘书人员应当努力做到这一点。同时，又不能“和而不为”，做个无用的秘书，必须为领导提供所需的参考，如何适当地向领导进言，既要有实际的价值，又要保证领导愉快接受，这需要一些技巧与艺术，值得深入揣摩，本文提出了四项原则，是笔者认为至关重要的，秘书人员违背其中任何一条都有可能破坏与领导的和谐关系，当然还有其他原则，篇幅所限，不再介绍。

Figure 2. 21-years sliding correlation between Indian and North China JJAS rainfall based on two members of historical (a) CCSM4 and(b) CNRM-CM5 model simulations. Note: The horizontal dashed lines denote the 95% con fidence level of the correlation coefficient according to the Student’s t-test.

3.2. Internal variability

The possibility of contribution from internal atmospheric variability to long-term changes in the Indian–East Asian summer rainfall relationship is demonstrated by a 100-years simulation of an AGCM forced by climatological monthly mean SST and sea ice (derived from AMIP). The AGCM used is ECHAM5 (Roeckner et al. 2003), and we use a version with a triangular truncation at zonal wavenumber 63 (T63; equivalent to a horizontal resolution of 1.9°)and 19 sigma levels in the vertical direction. Figure 3(a)displays the correlation of the 21-years running window for the three pairs of JJAS rainfall time series. The domains for calculating the area-mean rainfall in the model are the same as in observations.

Given the impact of ENSO on both Indian and East Asian summer monsoon variability (Wang, Wu, and Lau 2001),ENSO is likely a factor involved in the long-term changes in the Indian–East Asian summer rainfall relationship through changes its impacts on either the Indian or East Asian summer monsoon (Kumar, Rajagopalan, and Cane 1999; Wu and Wang 2002). Hu et al. (2005) indicated that ENSO may reinforce the connection between Indian and North China summer rainfall variations. Wang and Huang(2006) noted that a weakened connection may correspond to weakened in fluences of ENSO on both Indian and North China rainfall variations. In addition, SST anomalies in other regions may affect the relationship. E.g. Yun, Lee, and Ha(2014) proposed that a strengthened zonal gradient of SST between the Indian Ocean, western Pacific, and eastern Pacific may be a possible cause of an enhanced contrast in convective precipitation between South Asia and East Asia. Lee et al. (2017) suggested an enhanced in fluence of tropical Atlantic SSTs on Korean summer rainfall variations since the mid-1970s.

The difference in the rainfall relationship between the two periods is explained well by the circulation difference in the model. During model years 2040–2060, an obvious wave pattern is present across continental midlatitude Asia, with an anomalous high over central and East Asia(Figure 3(d)), which is similar to observed (Wu 2002). The anomalous high over East Asia induces southerly winds over North China and northerly winds over Japan, leading to above-normal and below-normal rainfall, respectively,in the two regions (Figure 3(b)). During model years 2012–2032, the wave pattern is weak and shifts eastward (Figure 3(e)). The anomalous high over East Asia shifts to Japan.Anomalous lower-level high pressure is situated southeast of Japan (not shown) and anomalous southwesterly winds along the west flank of the anomalous high transport more moisture from lower latitudes, contributing to above-normal rainfall over Japan (Figure 3(e)). As there are no yearto-year changes in the SST forcing, the long-term change in the above relationship in the AGCM simulation is attributable to the impacts of internal atmospheric variability.

Figure 3. (a) 21-years sliding correlation between Indian and North China (red curve), Indian and southern Japan (green curve), and North China and southern Japan (blue curve) JJAS rainfall in a 100-years AGCM simulation. (b, c) Correlation coefficients of rainfall with respect to normalized area-mean Indian rainfall during JJAS for the period 2040–2060 and 2012–2032 of the AGCM simulation. (d, e)Anomalies of geopotential height (units: m) at 200 hPa obtained by regression on normalized area-mean Indian rainfall during JJAS for the period 2040–2060 and 2012–2032 of the AGCM simulation.

3.3. Mean state changes

The atmospheric circulation patterns connecting the variations of Indian and East Asian summer rainfall depend upon the forcing and mean circulation. Thus, it is possible that changes in anomalous forcing and mean winds may lead to fluctuations in the Indian–East Asian rainfall connection. Wu and Jiao (2017) showed that the Indian–North China summer rainfall relationship tends to be stronger when a larger Indian rainfall anomaly occurs during a higher mean rainfall period. Lin, Lu, and Wu (2017) performed experiments using a barotropic vorticity equation model with the same anomalous heating over India but different mean winds prescribed over the midlatitudes.They found a notable difference in the midlatitude zonal wave pattern and in the location of the accompanying anomalous anticyclone over East Asia.

在2015年，“网络空间安全”被增设为一级学科，网络空间安全从此作为一级学科开启它的专业发展之路，但由于当前阶段，已有专业的知识体系和信息安全技术自身的迅猛发展等主客观原因，导致出现了一些问题。

3.4. Stochastic processes

The relationship between summer rainfall variations over India and East Asia displays long-term changes in both observations and climate model simulations, and there are different perspectives on what may have contributed to these long-term changes. ENSO’s impact may be a factor, but it cannot totally explain the observed long-term change in the Indian–North China summer rainfall relationship. Both a climatological SST–forced AGCM simulation and CMIP5 coupled model simulations indicate an important role played by internal atmospheric variability in the change of the Indian–North China summer rainfall relationship; plus, Monte Carlo test indicates that the role of random processes in the observed long-term changes of the Indian–East Asian summer rainfall relationship cannot be totally excluded.

不得不问，在学习的过程中，我们究竟为什么就这么偏爱背诵呢？显然是为了应对考试，或者说是为了方便实用。在背诵这些东西时，我们从来不必怀疑它们是否正确，要是不正确，我们何苦还要背诵它们？总之，我们想要背诵的东西一定是已然经过权威论证了的。记住它们就是以便更好地使用它们。久而久之，这样的学习的确是锻炼了我们的记忆力，但却也因此荒疏了我们的思考能力。常常，我们在行动上表现得相当果断，这恰是由于我们在思考上没那么复杂的缘故。基于此，我们想当然地就将人家哈姆雷特理解成了思想上的巨人，行动上的侏儒。

For the Indian–North China rainfall correlation, the maximum and minimum correlation coefficients in the observations are 0.80 and 0.01, respectively, which are around the 99% and 5% level of the Monte Carlo correlation distribution, respectively (Figure 4(a)). For the Indian–southern Japan rainfall correlation, the observed maximum and minimum correlation coefficient is close to the 95% and 2% level, respectively (Figure 4(b)). For the North China–southern Japan rainfall correlation, the maximum correlation coefficient in observations is below the 95% level (Figure 4(c)). These results indicate that the possibility that the observed change in the Indian–North China summer rainfall correlation being due to stochastic processes cannot be excluded.

Figure 4. Probability distribution (%) for the frequency of occurrence of correlation coefficients based on 5000 samples derived from a Monte Carlo test using (a) Indian and North China,(b) Indian and southern Japan, and (c) North China and southern Japan JJAS rainfall time series. Notes: The red vertical lines denote the minimum, mean and maximum correlation coefficient in observations. The black vertical lines denote the 1%,5%, 95%, and 99% level of the correlation coefficient from the Monte Carlo simulations.

4. Concluding remarks

Interdecadal changes in the relationship may occur due to stochastic processes (e.g. Gershunov, Schneider, and Barnett 2001) or sampling variability (e.g. Cash et al. 2017).To examine the impact of stochastic processes on the relationship, we carried out Monte Carlo test, as in Wu (2016)and Wu and Jiao (2017). Taking the Indian–North China rainfall relationship as an example, the procedure for the Monte Carlo test was as follows. First, two values were randomly selected from the Indian and North China summer rainfall time series for the period 1900–2010 (one from each time series). This was repeated 21 times to obtain two sub–time series with a length of 21 years. Then, we calculated the correlation coefficient between the two 21-years time series. The above processes were repeated 5000 times to obtain 5000 correlation coefficients. After that,we calculated the probability distribution of the correlation coefficient based on the 5000 values obtained above.The distribution was then compared to the maximum and minimum 21-years sliding correlation coefficients in the observations to determine the probability for the observed correlation to occur randomly. A similar procedure was applied to the Indian and southern Japan summer rainfall time series and the North China and southern Japan summer rainfall time series. The results are shown in Figure 4. Wu and Jiao (2017) performed similar Monte Carlo test for the Indian–North China rainfall relationship.

It is possible that different factors may play their respective roles in interdecadal changes in the above relationship at different times. Further analysis of observations and numerical model simulations are needed to advance our understanding of the contributions of different factors in the long-term changes in the relationship between Indian and East Asian summer rainfall.

九边其他诸镇或为传统边防重地，或为防卫京师而建，但延绥、固原、山西三镇最初却地处腹里，是在蒙古部落逐渐进入河套、明初的北边防御体系瓦解的情况下，成为防御蒙古各部的前沿要塞。

Disclosure statement

No potential conflict of interest was reported by the authors.

为了系统考察法律环境完善程度对水利产业集聚水平的影响，本文在控制经济规模、城市化水平、政府干预、收入差距、FDI、交通基础设施、劳动力素质和市场化程度等变量的基础上，重点考察区域法律环境指数（包括律师、会计师等市场中介组织服务条件，行业协会对企业的帮助程度，对生产者、消费者合法权益的保护程度，对知识产权保护的程度）对其产业集聚水平的所起作用的效应。本文采用的计量模型如下式所示。

Funding

This study was supported by the National Key Research and Development Program of China [grant number 2016YFA0600603];the National Key Basic Research Program of China [grant number 2014CB953902]; and the National Natural Science Foundation of China [grant number 41661144016], [grant number 41530425], [grant number 41475081], and [grant number 41275081].

以上高压设备的选型均依据于《工业与民用配电设计手册》[5]，且均为目前市场上比较流通的型号，可满足动、热稳定校验等条件。综上所述，高压电气设备选型如表4所示。

References

Cash, B. A., R. Barimalala, J. L. Kinter III, E. L. Altshuler, M. J.Fennessy, J. V. Manganello, F. Molteni, P. Towers, and F.Vitart. 2017. “Sampling Variability and the Changing ENSO-monsoon Relationship.” Climate Dynamics 48 (11-12): 4071–4079. doi:10.1007/s00382-016-3320-3.

Ding, Q., and B. Wang. 2005. “Circumglobal Teleconnection in the Northern Hemisphere Summer.” Journal of Climate 18:3483–3505.

Enomoto, T., B. J. Hoskins, and Y. Matsuda. 2003. “The Formation Mechanism of the Bonin High in August.” Quarterly Journal of Royal Meteorological Society 129: 157–178.

Gershunov, A., N. Schneider, and T. Barnett. 2001. “Lowfrequency Modulation of the ENSO–Indian Monsoon Rainfall Relationship: Signal or Noise?” Journal of Climate 14: 2486–2492.

Greatbatch, R. J., X. Sun, and X.-Q. Yang. 2013. “Impact of the Variability in the Indian Summer Monsoon on the East Asian Summer Monsoon.” Atmospheric Science Letters 14: 14–19.

Guo, Q.-Y. 1992. “Teleconnection between the Floods/Droughts in North China and Indian Summer Monsoon Rainfall.” Acta Geographica Sinica (in Chinese) 47 (5): 394–402.

Guo, Q.-Y., and J.-Q. Wang. 1988. “A Comparison of the Summer Precipitation in India with That in China.” Journal of Tropical Meteorology (in Chinese) 4: 53–60.

Ha, K.-J., Y.-W. Seo, J.-Y. Lee, R. H. Kripalani, and K.-S. Yun. 2017.“Linkages Between the South and East Asian Summer Monsoons: A Review and Revisit.” Climate Dynamics.doi:10.1007/s00382-017-3773-z.

Hu, Z.-Z., R. Wu, J. L. Kinter III, and S. Yang. 2005. “Connection of Summer Rainfall Variations in South and East Asia: Role of El Niño-Southern Oscillation.” International Journal of Climatology 25: 1279–1289.

Huang, R.-H., and Y. Wu. 1989. “The In fluence of ENSO on the Summer Climate Change in China and Its Mechanism.”Advances in Atmospheric Science 6: 21–32.

Kim, B.-J., S.-E. Moon, R. Lu, and R. H. Kripalani. 2002.“Teleconnections: Summer Monsoon Over Korea and India.”Advances in Atmospheric Sciences 19: 665–676.

Kripalani, R. H., and A. Kulkarni. 2001. “Monsoon Rainfall Variations and Teleconnections Over South and East Asia.”International Journal of Climatology 21: 603–616.

Kripalani, R. H., and S. V. Singh. 1993. “Large-scale Aspects of India-China Summer Rainfall.” Advances in Atmospheric Sciences 10: 71–84.

Kripalani, R. H., A. Kulkarni, and S. V. Singh. 1997. “Association of the Indian Summer Monsoon with the Northern Hemisphere Mid-latitude Circulation.” International Journal of Climatology 17: 1055–1067.

Krishnan, R., and M. Sugi. 2001. “Baiu Rainfall Variability and Associated Monsoon Teleconnections.” Journal of the Meteorological Society of Japan 79: 851–860.

Kumar, K. K., B. Rajagopalan, and M. A. Cane. 1999. “On the Weakening Relationship Between the Indian Monsoon and ENSO.” Science 284: 2156–2159.

Lee, J.-Y., M.-H. Kwon, K.-S. Yun, S.-K. Min, I.-H. Park, Y.-G. Ham, E.K. Jin, J.-H. Kim, K.-H. Seo, W.-M. Kim, S.-Y. Yim, and J.-H. Yoon.2017. “The Long-term Variability of Changma in the East Asian Summer Monsoon System: A Review and Revisit.” Asia-Pacific Journal of Atmospheric Science 53 (2): 252–272.

Lin, Z., R. Lu, and R. Wu. 2017. “Weakened Impact of the Indian Early Summer Monsoon on North China Rainfall around the Late 1970s: Role of Basic-state Change.” Journal of Climate 30(19): 7991–8005. doi:10.1175/JCLI-D-17-0036.1.

Liu, Y.-Y., and Y.-H. Ding. 2008. “Analysis and Numerical Simulation of the Teleconnection between Indian Summer Monsoon and Precipitation in North China.” Acta Meteorologica Sinica(in Chinese) 66 (5): 789–799.

Lu, R.-Y., J.-H. Oh, and B.-J. Kim. 2002. “A Teleconnection Pattern in Upper-level Meridional Wind over the North African and Eurasian Continent in Summer.” Tellus 54A: 44–55.

Preethi, B., M. Mujumdar, R. H. Kripalani, A. Prabhu, and R.Krishnan. 2017a. “Recent Trends and Tele-connections Among South and East Asian Summer Monsoons in a Warming Environment.” Climate Dynamics 48 (7–8): 2489–2505. doi:10.1007/S00382-016-3218-0.

Preethi, B., M. Mujumdar, A. Prabhu, and R. H. Kripalani. 2017b.“Variability and Teleconnections of South and East Asian Summer Monsoons in Present and Future Projections of CMIP5 Climate Models.” Asia Pacific Journal of Atmospheric Sciences 53(2): 305–325.

Rayner, N. A., B. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander,D. P. Rowell, E. C. Kent, and A. Kaplan. 2003.“Global Analyses of Sea Surface Temperature, Sea Ice, and Night Marine Air Temperature since the Late Nineteenth Century.” Journal Geophysiscal Research 108 (D14): 4407. doi:10.1029/2002JD002670.

Roeckner, E., G. Bäuml, L. Bonaventura, R. Brokopf, M. Esch,M. Giorgetta, S. Hagemann, et al. 2003. “The Atmospheric General Circulation Model ECHAM5. Part I: Model Description.”Max Planck Institute for Meteorology Rep. 349, 127 Pp.[Available from MPI for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany.].

Schneider, U., A. Becker, P. Finger, A. Meyer-Christoffer, B. Rudolf,and M. Ziese. 2015. “GPCC Full Data Reanalysis Version 7.0 at 0.5°: Monthly Land Surface Precipitation from Rain Gauges Built on GTS-based and Historic Data.” doi:10.5676/DWD_GPCC/FD_M_V7_050.

Walker, G. T. 1923. “Correlation in Seasonal Variations of Weather VIII: A Preliminary Study of World Weather.” Memorial Indian Meteorology Department 24: 75–131.

Wang, S.-W., and J.-B. Huang. 2006. “Instability of Precipitation Teleconnection Between North China and India.” Progress Natural Science (in Chinese) 16 (8): 980–985.

Wang, B., R. Wu, and K.-M. Lau. 2001. “Interannual Variability of the Asian Summer Monsoon: Contrasts Between the Indian and Western North Pacific-East Asian Monsoons.” Journal of Climate 14: 4073–4090.

Webster, P. J., V. O. Magaña, T. N. Palmer, R. A. Thomas, M. Yanai,and T. Yasunari. 1998. “Monsoons: Processes, Predictability,and the Prospects for Prediction.” Journal of Geophysical Research 103: 14451–14510.

Wu, R. 2002. “A mid-Latitude Asian Circulation Pattern in Boreal Summer and Its Connection with the Indian and East Asian Summer Monsoons.” International Journal of Climatology 22:1879–1895.

Wu, R. 2016. “Possible Roles of Regional SST Anomalies in Long-term Changes in the Relationship between the Indian and Australian Summer Monsoon Rainfall.” Theoretical and Applied Climatology 124 (3–4): 663–677.

Wu, R. 2017. “Relationship between Indian and East Asian Summer Rainfall Variations.” Advances in Amospheric Science 34 (1): 4–15. doi:10.1007/s00376-016-6216-6.

Wu, R., and Y. Jiao. 2017. “The Impacts of the Indian Summer Rainfall on North China Summer Rainfall.” Asia Pacific Journal of Atmospheric Sciences 53 (2): 195–206. doi:10.1007/s13143-017-0013-8.

Wu, R., and B. Wang. 2002. “A Contrast of the East Asian Summer Monsoon and ENSO Relationship between 1962–77 and 1978–93.” Journal of Climate 15: 3266–3279.

Wu, R., Z.-Z. Hu, and B. P. Kirtman. 2003. “Evolution of ENSO-related Rainfall Anomalies in East Asia.” Journal of Climate 16:3741–3757.

Yun, K.-S., J.-Y. Lee, and K.-J. Ha. 2014. “Recent Intensification of the South and East Asian Monsoon Contrast Associated with an Increase in the Zonal Tropical SST Gradient.” Journal of Geophysical Research 119: 8104–8116.

Zhang, R. 2001. “Relation of Water Vapor Transport from the Indian Monsoon with That over East Asia and the Summer Rainfall in China.” Advances in Atmospheric, Science 18: 1005–1017.

Zhang, R.-H., A. Sumi, and M. Kimoto. 1999. “A Diagnostic Study of the Impact of El Niño on the Precipitation in China.”Advances in Atmospheric Science 16: 229–241.