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ORIGINAL RESEARCH article

Front. Environ. Sci.
Sec. Interdisciplinary Climate Studies
Volume 12 - 2024 | doi: 10.3389/fenvs.2024.1503141
This article is part of the Research Topic Climate change impacts on agriculture and natural resources along with adaptation options with a focus on extreme events View all articles

Assessment of extreme climate stress across China's maize harvest region in CMIP6 simulations

Provisionally accepted
Xinmin Chen Xinmin Chen 1Zexu Shi Zexu Shi 1Dengpan Xiao Dengpan Xiao 1*Yang Lu Yang Lu 1Huizi Bai Huizi Bai 2Man Zhang Man Zhang 1Dandan REN Dandan REN 3Yongqing Qi Yongqing Qi 4Shikai Song Shikai Song 1
  • 1 Hebei Normal University, Shijiazhuang, China
  • 2 Hebei Academy of sciences, Shijiazhuang, Hebei Province, China
  • 3 Linyi University, Linyi, Shandong Province, China
  • 4 Chinese Academy of Sciences (CAS), Beijing, Beijing, China

The final, formatted version of the article will be published soon.

    Climate change is expected to increase the frequency and severity of climate extremes, which will negatively impact crop production. As one of the main food and feed crops, maize is also vulnerable to extreme climate events. In order to accurately and comprehensively assess the future climate risk to maize, it is urgent to project and evaluate the stress of extreme climate related maize production under future climate scenarios. In this study, we comprehensively evaluated the spatio-temporal changes in the frequency and intensity of six extreme climate indices (ECIs) across China's maize harvest region by using a multi-model ensemble method, and examined the capability of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to capture these variations. We found that the Independence Weight Mean (IWM) ensemble results calculated by multiple Global Climate Models (GCMs) with bias correction could better reproduce each ECI.The results indicated that heat stress for maize showed consistent increase trends under four future climate scenarios in the 21st century. The intensity and frequency of the three extreme temperature indices in 2080s were significantly higher than these in 2040s, and in the high emission scenario were significantly higher than these in the low emission scenario. The three extreme precipitation indices changed slightly in the future, but the spatial changes were more significant. Therefore, with the uncertainty of climate change and the differences of climate characteristics in different regions, the optimization of specific management measures should be considered in combination with the specific conditions of future local climate change.

    Keywords: extreme climate, Global climate model, Multi-model ensemble, Maize, CMIP6

    Received: 28 Sep 2024; Accepted: 18 Nov 2024.

    Copyright: © 2024 Chen, Shi, Xiao, Lu, Bai, Zhang, REN, Qi and Song. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Dengpan Xiao, Hebei Normal University, Shijiazhuang, China

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