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A stepwise-clustered copula downscaling approach for ensemble analyses of discrete and interactive features in precipitation-extreme variations: a case study for eastern China

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Abstract

Extreme precipitation events are frequent under global warming, leading to severe social, economic, and environmental damages. Therefore, a coupled stepwise clustered copula downscaling (SCCD) method is developed to explore the spatial and temporal variability of extreme precipitation in Eastern China under two shared socioeconomic pathways (SSPs). The performance of SCCD in reproducing historical climatology is assessed by comparing its simulated values with the observed data. The result demonstrates that SCCD performs well in modeling climate variables. In addition, the seasonal variations, probability distributions, and absolute changes of nine extreme precipitation indices in the future periods (i.e., 2024—2060 and 2061—2100) are compared with their performance in the historical period (i.e., 1981—2020). The results show that the precipitation in Eastern China shows an increasing trend over time. For example, compared to the historical period, the mean annual precipitation increases by 9.4% and 13.6% for the periods 20424–2060 and 2060–2100 under SSP585, respectively. The spatial variability and age trends suggest that future precipitation extremes also show significant regional differences. By the end of the twenty-first century, both frequencies and intensities of extreme precipitation at high latitudes have changed significantly. Under both SSP scenarios, all nine indices (except Consecutive Dry Days) increase by more than 10% in the future period at the high latitudes, especially for Very heavy precipitation days (R20), where the increase is more than 50% in most areas. In addition, the impacts of different emission scenarios on precipitation in autumn and winter are significantly greater than those in spring and summer. The results can provide a scientific basis for decision-makers to develop mitigation and adaptation policies to minimize the risks caused by climate change.

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Data availability

The observations are available from the China Meteorological Data Service Center (CMDC) website (http://data.cma.cn). The GCM outputs are obtained from Earth System Grid Federation (ESGF) (https://esgf-node.llnl. gov). The ERA5 reanalysis dataset is extracted from the ECMWF MARS ERA5 types (https://cds.climate.copernicus.eu). All data generated or analyzed during this study are included in this published article.

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Acknowledgements

This research was supported by the Natural Science Foundation (U2040212, 52221003, 52279002, 52279003) and the Natural Science and Engineering Research Council of Canada. We are also very grateful for the helpful inputs from the Editor and anonymous reviewers.

Funding

This research was supported by the Natural Science Foundation (U2040212, 52221003, 52279002, 52279003) and the Natural Science and Engineering Research Council of Canada.

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Authors and Affiliations

  1. China-Canada Center of Energy, Environment and Sustainability Research, UR-SDU, School of Environmental Science and Engineering, Shandong University, Qingdao, 266237, China

    Bizheng Yu, Guohe Huang, Shuguang Wang & Wenshu Kuang

  2. Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, SK, S4S 0A2, Canada

    Guohe Huang

  3. State Key Joint Laboratory of Environmental Simulation and Pollution Control, China-Canada for Energy, Environment and Ecology Research, UR-BNU, School of Environment, Beijing Normal University, Beijing, 100875, China

    Xiong Zhou & Yongping Li

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  1. Bizheng Yu
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Contributions

Yu BZ did all the calculations and analysis and wrote and edited the manuscript. Huang GH designed and directed the project. Zhou X participated in reviewing and editing the manuscript. Wang SG and Li YP provided some advice for the project. Ren JY and Kuang WS provided the instruction in SCA and copula.

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Correspondence to Guohe Huang.

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Yu, B., Huang, G., Zhou, X. et al. A stepwise-clustered copula downscaling approach for ensemble analyses of discrete and interactive features in precipitation-extreme variations: a case study for eastern China. Clim Dyn (2024). https://doi.org/10.1007/s00382-024-07260-9

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