Climate Change Research ›› 2019, Vol. 15 ›› Issue (6): 584-595.doi: 10.12006/j.issn.1673-1719.2019.023

• Changes in Climate System • Previous Articles     Next Articles

The influence of data processing on constructing regional average precipitation time series

Yun-Jian ZHAN1,Guo-Yu REN2,3(),Peng-Ling WANG2   

  1. 1 National Meteorological Information Center, China Meteorological Administration, Beijing 100081, China
    2 National Climate Center, China Meteorological Administration, Beijing 100081, China
    3 Department of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
  • Received:2019-01-24 Revised:2019-03-27 Online:2019-11-30 Published:2019-11-30
  • Contact: Guo-Yu REN


The selection of different anomaly indicators will make a significant difference to study the long-term variation of precipitation in a wide range of areas using surface observation data. Daily precipitation data of 2139 national stations in the Chinese mainland were used to construct the regional average time series of precipitation, precipitation days and precipitation intensity, obtained from different indicators. These series were compared to explore the deviation of the long-term trend estimation in precipitation for western China, eastern China, and the entire China. The results shows that from 1951 to 1957, the regional average raw values of precipitation, precipitation days, and precipitation intensities in China had spuriously high biases due to the lack of data from stations in western China, which caused large deviations in the linear trend estimates from 1951 to 2016. In western China, the time series of the regional average precipitation anomaly percentages had excessive fluctuations. The changes in series of anomalies and normalized anomalies were reasonable in every region. The regional average time series of the original values and anomaly values of precipitation amount and days of the entire China basically reflected the precipitation changes in the humid regions in eastern China, while the precipitation anomaly percentages were mainly composed of the precipitation changes in the arid regions. Normalized anomaly could comprehensively reflect the precipitation changes in humid regions and arid regions.

Key words: Regional average, Indicator, Precipitation, Time series, Climate change

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