Climate Dynamics

, Volume 52, Issue 5–6, pp 2943–2957 | Cite as

Prediction of summer hot extremes over the middle and lower reaches of the Yangtze River valley

  • Kai Yang
  • Jingyong ZhangEmail author
  • Lingyun Wu
  • Jiangfeng Wei


Substantial efforts have been made in recent decades to understand the characteristics and variations of summer hot extremes and the underlying physical processes involved. However, the seasonal prediction of summer hot extremes remains challenging. The populous middle and lower reaches of the Yangtze River valley (MLYR) in China are severely influenced by hot extremes during summer. This study presents seasonal predictions of summer hot extremes over the MLYR for the period 1979–2016 based on three preceding predictors that are closely linked to hot extremes over this region: spring soil moisture over the southeastern Indochina Peninsula (SIP); spring sea surface temperature (SST) over the western tropical Pacific (WTP); and the difference of Nino 3.4 SST data in May and the previous December. The soil moisture deficit over the SIP and warm SST over the WTP in spring, as well as the difference of Niño 3.4 SST data in May and the previous December, tend to result in positive geopotential height anomalies over the MLYR, which may favor hot extremes by enhancing downward solar radiation, subsidence warming and local soil moisture–temperature coupling associated with precipitation reduction. Using these three predictors, we demonstrate with cross validation that the temporal variations of hot extremes over the MLYR can be skillfully predicted for the study period (i.e., 1979–2016), while biases exist in the magnitude. Hindcast experiments for 2012–2016 show that high prediction skill can be achieved for the spatial patterns of hot extremes, with pattern correlation coefficients of 0.83–0.99. Our findings are expected to facilitate the practical prediction of hot extremes over the MLYR.


Seasonal prediction Summer hot extremes Soil moisture Sea surface temperature ENSO 



We would like to thank the reviewers for their helpful comments. The ERA-Interim data used in this study were obtained from the ECMWF data server: This work was supported by the National Key Research and Development Program of China (2018YFA0606501 and 2017YFA0603601), the National Natural Science Foundation of China (Grant no. 41675085), and the Chinese Academy of Sciences “The Belt and Road Initiatives” Program on International Cooperation: Climate Change Research and Observation Project (134111KYSB20160010).

Supplementary material

382_2018_4302_MOESM1_ESM.docx (1.8 mb)
Supplementary material 1 (DOCX 1882 KB)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Kai Yang
    • 1
    • 2
  • Jingyong Zhang
    • 1
    • 2
    Email author
  • Lingyun Wu
    • 3
  • Jiangfeng Wei
    • 4
  1. 1.Center for Monsoon System Research, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  4. 4.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education/International Joint Research Laboratory on Climate and Environment ChangeNanjing University of Information Science and TechnologyNanjingChina

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