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Combined Impacts of Climate Variability Modes on Seasonal Precipitation Extremes Over China

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Abstract

The joint effects of natural climate variability on the variations in seasonal precipitation extremes across China during 1961–2017 were studied based on a non-stationary GEV (generalized extreme value) model with parameters depending on multiple large-scale modes. Spatial analysis results show that the individual climate variability mode tends to have similar but weaker impacts on seasonal extremes than on mean rainfall. The combined effects of multiple large-scale modes are more likely to trigger a stronger control on the upper tail of the precipitation distribution than on mean rainfall in specific seasons. The distribution of seasonal precipitation extremes exhibits evident nonuniformity over China in different phases of the large-scale modes. Notably, the statistically significant positive responses of RX1day (maximum 1-day precipitation) and RX5day (maximum 5-day precipitation) to the El Niño–Southern Oscillation (ENSO) and Atlantic Multidecadal Oscillation (AMO) are observed at 1481 and 1416 grid points, respectively, which is more than the result (822) for SDII (simple daily intensity index). Moreover, the combined effects of ENSO and AMO on RX1day and RX5day are 10 times greater than on mean rainfall. The combined influences of three large-scale modes of climate variability on extreme precipitation events are stronger than those on mean rainfall across China in all four seasons. These phenomena suggest a closer relationship between the joint influences of multiple large-scale modes and the occurrence of seasonal precipitation extremes over China. The findings in this study will be helpful for the seasonal prediction of regional precipitation extremes and evaluating the ability of climate models to capture these teleconnection relationships.

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Acknowledgements

We are grateful to the constructive comments and suggestions from the editor and anonymous reviewers, which have greatly helped improve the quality of this work. The helpful comments and suggestions from Dr. Seung-Ki Min and Xiaolong Chen are appreciated. Daily observations were collected from the China Meteorological Data Service Center (http://data.cma.cn). The data on the large-scale modes of climate variability were provided by NOAA from their website at https://origin.cpc.ncep.noaa.gov/products/precip/CWlink/MJO/climwx.shtml.

Funding

This study was jointly funded by the Key Research and Development Plan of Shandong Province in 2019 (Grant No. 2019GGX105021), the Natural Science Foundation and Sci-tech Development Project of Shandong Province (Grant No. ZR2018MD014; J18KA210), and the China Postdoctoral Science Foundation (Grant No. 119100582H; 1191005830).

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Authors and Affiliations

  1. College of Urban Construction, Heze University, Heze, 274000, China

    Tao Gao

  2. Department of Biostatistics, Bioinformatics & Biomathematics, Georgetown University, Washington D.C., 20057, USA

    Yifei Xu

  3. Department of Statistics, George Washington University, Washington D.C., 20052, USA

    Huixia Judy Wang

  4. Pacific Climate Impacts Consortium, University of Victoria Victoria, British Columbia, Canada

    Qiaohong Sun

  5. Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC, USA

    Lian Xie

  6. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China

    Tao Gao

  7. School of Geoscience, Shanxi Normal University, Linfen, 041004, Shanxi, China

    Fuqiang Cao

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  1. Tao Gao
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Contributions

T G: Investigation, Conceptualization, Methodology, Writing – Reviewing and Editing, Supervision, Funding acquisition. Y X: Formal analysis, Investigation, Writing – Original Draft. H W: Formal analysis, Investigation, Writing – Original Draft. Q S, L X, and F C: Investigation, Writing – Reviewing and Editing.

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Correspondence to Tao Gao.

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Gao, T., Xu, Y., Wang, H.J. et al. Combined Impacts of Climate Variability Modes on Seasonal Precipitation Extremes Over China. Water Resour Manage 36, 2411–2431 (2022). https://doi.org/10.1007/s11269-022-03150-z

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