Skip to main navigation Skip to search Skip to main content

Precipitation From Persistent Extremes is Increasing in Most Regions and Globally

  • Haibo Du
  • , Lisa V. Alexander
  • , Markus G. Donat
  • , Tanya Lippmann
  • , Arvind Srivastava
  • , Jim Salinger
  • , Andries Kruger
  • , Gwangyong Choi
  • , Hong S. He
  • , Fumiaki Fujibe
  • , Matilde Rusticucci
  • , Banzragch Nandintsetseg
  • , Rodrigo Manzanas
  • , Shafiqur Rehman
  • , Farhat Abbas
  • , Panmao Zhai
  • , Ibouraïma Yabi
  • , Michael C. Stambaugh
  • , Shengzhong Wang
  • , Altangerel Batbold
  • Priscilla Teles de Oliveira, Muhammad Adrees, Wei Hou, Shengwei Zong, Claudio Moises Santos e Silva, Paulo Sergio Lucio, Zhengfang Wu*
*Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

103 Downloads (Pure)

Abstract

Extreme precipitation often persists for multiple days with variable duration but has usually been examined at fixed duration. Here we show that considering extreme persistent precipitation by complete event with variable duration, rather than a fixed temporal period, is a necessary metric to account for the complexity of changing precipitation. Observed global mean annual-maximum precipitation is significantly stronger (49.5%) for persistent extremes than daily extremes. However, both globally observed and modeled rates of relative increases are lower for persistent extremes compared to daily extremes, especially for Southern Hemisphere and large regions in the 0-45°N latitude band. Climate models also show significant differences in the magnitude and partly even the sign of local mean changes between daily and persistent extremes in global warming projections. Changes in extreme precipitation therefore are more complex than previously reported, and extreme precipitation events with varying duration should be taken into account for future climate change assessments.

Original languageEnglish
Pages (from-to)6041-6049
Number of pages9
JournalGeophysical Research Letters
Volume46
Issue number11
Early online date21 May 2019
DOIs
Publication statusPublished - 16 Jun 2019

Funding

Data Set names: the CMIP5 data set (http://pcmdi9.llnl.gov/), the GHCND data set (https://www.ncdc.noaa.gov/ ghcn‐daily‐description), the ECA&D data set (https://www.ecad.eu/), the USHCN data set (http://cdiac.ess‐dive. lbl.gov/ftp/ushcn_daily/), and the data set for Canada (http://climate.weather. gc.ca/). Observed data set is available online (https://doi.org/10.5281/ zenodo.2573295). This work is jointly supported by the National Key R&D Program of China (2016YFA0602301), National Natural Science Foundation of China (41601052), Science and Technology Development Plan of Jilin Province (20190201291JC and 20180520098JH), the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 (RYC‐2017‐22964), the EU Horizon 2020 EUCP project (776613), and National University of Mongolia (P2017‐2504). Data Set names: the CMIP5 data set (http://pcmdi9.llnl.gov/), the GHCND data set (https://www.ncdc.noaa.gov/ghcn-daily-description), the ECA&D data set (https://www.ecad.eu/), the USHCN data set (http://cdiac.ess-dive.lbl.gov/ftp/ushcn_daily/), and the data set for Canada (http://climate.weather.gc.ca/). Observed data set is available online (https://doi.org/10.5281/zenodo.2573295). This work is jointly supported by the National Key R&D Program of China (2016YFA0602301), National Natural Science Foundation of China (41601052), Science and Technology Development Plan of Jilin Province (20190201291JC and 20180520098JH), the Spanish Ministry for the Economy, Industry and Competitiveness Ram?n y Cajal 2017 (RYC-2017-22964), the EU Horizon 2020 EUCP project (776613), and National University of Mongolia (P2017-2504).

FundersFunder number
National Key R&D Program of China2016YFA0602301
National University of MongoliaP2017‐2504
Science and Technology Development Plan of Jilin Province20190201291JC, 20180520098JH
Spanish Ministry for the Economy, Industry and Competitiveness Ram?n y Cajal
Spanish Ministry for the Economy, Industry and Competitiveness Ramón y CajalRYC‐2017‐22964
Horizon 2020 Framework Programme
National Natural Science Foundation of China41601052
Horizon 2020776613

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 13 - Climate Action
      SDG 13 Climate Action

    Fingerprint

    Dive into the research topics of 'Precipitation From Persistent Extremes is Increasing in Most Regions and Globally'. Together they form a unique fingerprint.

    Cite this