Meteorology and Atmospheric Physics

, Volume 129, Issue 3, pp 273–282 | Cite as

A new mean-extreme vector for the trends of temperature and precipitation over China during 1960–2013

  • Juan Li
  • Zhiwei Zhu
  • Wenjie Dong
Original Paper


A mean–extreme (M-E) vector is defined to combine the changes of climate means and extremes. The direction of the vertical axis represents changes in means, whereas the direction of the horizontal axis represents changes in extremes. Therefore, the M-E vector can clearly reflect both the amplitude and direction of changes in climate means and extremes. Nine types of M-E vectors are defined. They are named as MuEu, MuEd, MuEz, MdEu, MdEd, MdEz, MzEu, MzEd, and MzEz. Here M and E stand for climate means and extremes, respectively, whereas u, d, and z indicate an upward, downward trend and no trend, respectively. Both temperature mean and extremely high temperature days are consistently increased (MuEu) in nearly whole China throughout four seasons. However, the MuEd-type vector dominates in some regions. The MuEd-type vector appears over the Huang Huai river basin in spring, summer and winter. For the M-E vector of temperature mean and extremely low temperature days, the MuEd-type spreads the entire China for all seasons. The M-E vector for precipitation mean and the extreme precipitation days possesses identical trends (MuEu or MdEd) despite of seasons. The MuEu-type dominates in northeastern China and west of 105°E in spring, northwestern and central/southern China in summer, west of 100°E and northeastern China in autumn, and nearly whole China in winter. Precipitation mean and extreme precipitation days are all decreased (MdEd) in the rest of China for all reasons. The trends relationship in means and extremes over China presented herein could provide a scientific foundation to predict change of extremes using change of mean as the predictor.


Summer Precipitation Climate Extreme Extreme Index Ensemble Empirical Mode Decomposition Qinling Mountain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors thank three anonymous reviewers for their constructive comments and suggestions. This work was supported by the National Natural Science Foundation of China (41330527), the Natural Science Foundation of Jiangsu Province (BK20140046), and the priority academic program development of Jiangsu Higher Education institutions (PAPD). This is SOEST Contribution Number 9636 and IPRC Contribution Number 1196.


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Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  1. 1.Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)Nanjing University of Information Science and TechnologyNanjingChina
  2. 2.Department of Atmospheric Sciences, International Pacific Research CenterUniversity of Hawaii at ManoaHonoluluUSA
  3. 3.School of Atmospheric ScienceSun Yat-Sen UniversityGuangzhouChina

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