Rainfall seasonality changes and its possible teleconnections with global climate events in China

  • Shulin Deng
  • Ni Yang
  • Manchun LiEmail author
  • Liang Cheng
  • Zhenjie Chen
  • Yanming Chen
  • Tan Chen
  • Xiaoqiang Liu


Despite the importance of seasonal distribution and interannual variability of rainfall to the ecosystem and society, there is a lack of regional-level studies on rainfall seasonality, and teleconnections between global climate events and rainfall seasonality are not well understood. To address these issues, this study analyzed the spatial and temporal characteristics of rainfall seasonality across China from 1961 to 2012. A novel application of information theory-based rainfall seasonality indicators was conducted at the regional scale, and observed monthly precipitation data was used. The rainfall seasonality anomalies during negative and positive phases of the El Niño–Southern Oscillation, Indian Ocean Dipole, North Atlantic Oscillation, and Pacific Decadal Oscillation, and the possible physical mechanisms behind were also investigated. The results showed that rainfall seasonality increased, especially in Southeast China, which can be attributed to changes in magnitude (annual rainfall), timing, or duration of the wet season. Global climate events significantly affected rainfall seasonality indicators in Southeast China during negative and positive phases. The sea surface temperature (SST) or sea level pressure (SLP), and wind anomalies during the negative and positive phases might explain the spatial differences in the influences of global climate events on rainfall seasonality across China. These results may prove valuable for sustainable water resource management and agricultural production in China.


Rainfall seasonality Global climate events Monsoon region China 



This work is supported by the National Key R&D Plan (2017YFB0504205), General Program of National Natural Science Foundation (Grant No. 41571378), National Natural Science Foundation (Grant no. 41867071), the program B for Outstanding PhD candidate of Nanjing University (201801B036), 2017 annual young teachers basic capacity improvement project of universities in Guangxi (Grant no. 2017KY0597), and 2016 Young Teacher Research Development Fund Project in Guangxi University of Finance and Economics (Grant no. 2016QNB17). The software used to generate all the results are ArcGIS and MATLAB.

Supplementary material

382_2019_4722_MOESM1_ESM.docx (20 kb)
Supplementary material 1 (DOCX 19 KB)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Shulin Deng
    • 1
    • 3
    • 5
  • Ni Yang
    • 4
  • Manchun Li
    • 1
    • 2
    • 3
    Email author
  • Liang Cheng
    • 1
    • 2
    • 3
  • Zhenjie Chen
    • 2
    • 3
  • Yanming Chen
    • 2
    • 3
  • Tan Chen
    • 2
    • 3
  • Xiaoqiang Liu
    • 2
    • 3
  1. 1.Collaborative Innovation Center of South China Sea StudiesNanjing UniversityNanjingChina
  2. 2.Jiangsu Provincial Key Laboratory of Geographic Information Science and TechnologyNanjing UniversityNanjingChina
  3. 3.School of Geography and Ocean ScienceNanjing UniversityNanjingChina
  4. 4.School of Management Science and EngineeringGuangxi University of Finance and EconomicsNanningChina
  5. 5.School of Geography and PlanningNanning Normal UniversityNanningChina

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