Projected future changes in extreme climate indices affecting rice production in China using a multi-model ensemble of CMIP6 projections
Assessing and predicting the spatial-temporal characteristics of extreme climate events can effectively identify the impacts of climate change on crop production and propose targeted measures. This study systematically evaluates the intensity and spatiotemporal evolution of extreme climate events du...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1595367/full |
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| author | Xinmin Chen Dengpan Xiao Dengpan Xiao Yongqing Qi Yongqing Qi Zexu Shi Huizi Bai Yang Lu Yang Lu Man Zhang Man Zhang Peipei Pan Peipei Pan Dandan Ren Xiaomeng Yin Xiaomeng Yin Renjie Li Renjie Li |
| author_facet | Xinmin Chen Dengpan Xiao Dengpan Xiao Yongqing Qi Yongqing Qi Zexu Shi Huizi Bai Yang Lu Yang Lu Man Zhang Man Zhang Peipei Pan Peipei Pan Dandan Ren Xiaomeng Yin Xiaomeng Yin Renjie Li Renjie Li |
| author_sort | Xinmin Chen |
| collection | DOAJ |
| description | Assessing and predicting the spatial-temporal characteristics of extreme climate events can effectively identify the impacts of climate change on crop production and propose targeted measures. This study systematically evaluates the intensity and spatiotemporal evolution of extreme climate events during critical phenological stages in China’s major rice-growing regions based on 11 extreme climate indices (ECIs). The future climate data were obtained from 18 Global Climate Models (GCMs) integrated in the Coupled Model Intercomparison Project phase 6 (CMIP6) with four shared socio-economic pathways (SSPs) to project the future changes of ECIs related rice production. The results indicate that the multi-model ensemble constructed via the Independence Weighted Mean method (IWM) significantly outperformed both the arithmetic mean method (AM) and individual GCMs in replicating observed trends of 11 ECIs during the historical period (1981–2014), with notable reductions in root mean square error (RMSE) for certain indices. The projections reveal that under the SSP585 scenario, the duration of extreme heat events (e.g., HD) in southern China will increase by 12–18 days by the 2080s compared to the historical period (1981–2014), representing the highest increase among all scenarios. The extreme drought events (e.g., D-Vgp) in northeastern China are projected to reach 14.8, 9.7, and 9.7 days by the 2040s, further rising to 14.3, 10.0, and 10.3 days by the 2080s. The extreme precipitation events are predominantly concentrated in southwestern and southern China, with consecutive wet days (CWD) showing limited increase within 3 days. The findings highlight that China’s rice cultivation will face intensified extreme climate challenges in the future, particularly extreme heat stress, necessitating urgent adaptive strategies to mitigate the adverse impacts of climate change on rice production. |
| format | Article |
| id | doaj-art-bb4eda2ee2574dda8cd557e1480e0df4 |
| institution | DOAJ |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Plant Science |
| spelling | doaj-art-bb4eda2ee2574dda8cd557e1480e0df42025-08-20T02:40:51ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-07-011610.3389/fpls.2025.15953671595367Projected future changes in extreme climate indices affecting rice production in China using a multi-model ensemble of CMIP6 projectionsXinmin Chen0Dengpan Xiao1Dengpan Xiao2Yongqing Qi3Yongqing Qi4Zexu Shi5Huizi Bai6Yang Lu7Yang Lu8Man Zhang9Man Zhang10Peipei Pan11Peipei Pan12Dandan Ren13Xiaomeng Yin14Xiaomeng Yin15Renjie Li16Renjie Li17College of Geography Science, Hebei Normal University, Shijiazhuang, ChinaCollege of Geography Science, Hebei Normal University, Shijiazhuang, ChinaHebei Laboratory of Environmental Evolution and Ecological Construction, College of Geography Science, Hebei Normal University, Shijiazhuang, ChinaKey Laboratory for Agricultural Water Resources, Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang, ChinaHebei Key Laboratory for Agricultural Water Saving, Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang, ChinaCollege of Geography Science, Hebei Normal University, Shijiazhuang, ChinaEngineering Technology Research Center, Geographic Information Development and Application of Hebei, Institute of Geographical Sciences, Hebei Academy of Sciences, Shijiazhuang, ChinaCollege of Geography Science, Hebei Normal University, Shijiazhuang, ChinaHebei Laboratory of Environmental Evolution and Ecological Construction, College of Geography Science, Hebei Normal University, Shijiazhuang, ChinaCollege of Geography Science, Hebei Normal University, Shijiazhuang, ChinaHebei Laboratory of Environmental Evolution and Ecological Construction, College of Geography Science, Hebei Normal University, Shijiazhuang, ChinaCollege of Geography Science, Hebei Normal University, Shijiazhuang, ChinaHebei Laboratory of Environmental Evolution and Ecological Construction, College of Geography Science, Hebei Normal University, Shijiazhuang, ChinaSchool of Resources and Environment, College of Carbon Neutrality, Linyi University, Linyi, ChinaCollege of Geography Science, Hebei Normal University, Shijiazhuang, ChinaHebei Laboratory of Environmental Evolution and Ecological Construction, College of Geography Science, Hebei Normal University, Shijiazhuang, ChinaCollege of Geography Science, Hebei Normal University, Shijiazhuang, ChinaHebei Laboratory of Environmental Evolution and Ecological Construction, College of Geography Science, Hebei Normal University, Shijiazhuang, ChinaAssessing and predicting the spatial-temporal characteristics of extreme climate events can effectively identify the impacts of climate change on crop production and propose targeted measures. This study systematically evaluates the intensity and spatiotemporal evolution of extreme climate events during critical phenological stages in China’s major rice-growing regions based on 11 extreme climate indices (ECIs). The future climate data were obtained from 18 Global Climate Models (GCMs) integrated in the Coupled Model Intercomparison Project phase 6 (CMIP6) with four shared socio-economic pathways (SSPs) to project the future changes of ECIs related rice production. The results indicate that the multi-model ensemble constructed via the Independence Weighted Mean method (IWM) significantly outperformed both the arithmetic mean method (AM) and individual GCMs in replicating observed trends of 11 ECIs during the historical period (1981–2014), with notable reductions in root mean square error (RMSE) for certain indices. The projections reveal that under the SSP585 scenario, the duration of extreme heat events (e.g., HD) in southern China will increase by 12–18 days by the 2080s compared to the historical period (1981–2014), representing the highest increase among all scenarios. The extreme drought events (e.g., D-Vgp) in northeastern China are projected to reach 14.8, 9.7, and 9.7 days by the 2040s, further rising to 14.3, 10.0, and 10.3 days by the 2080s. The extreme precipitation events are predominantly concentrated in southwestern and southern China, with consecutive wet days (CWD) showing limited increase within 3 days. The findings highlight that China’s rice cultivation will face intensified extreme climate challenges in the future, particularly extreme heat stress, necessitating urgent adaptive strategies to mitigate the adverse impacts of climate change on rice production.https://www.frontiersin.org/articles/10.3389/fpls.2025.1595367/fullrice systemextreme climate indicesmulti-model ensembleglobal climate modelCMIP6 |
| spellingShingle | Xinmin Chen Dengpan Xiao Dengpan Xiao Yongqing Qi Yongqing Qi Zexu Shi Huizi Bai Yang Lu Yang Lu Man Zhang Man Zhang Peipei Pan Peipei Pan Dandan Ren Xiaomeng Yin Xiaomeng Yin Renjie Li Renjie Li Projected future changes in extreme climate indices affecting rice production in China using a multi-model ensemble of CMIP6 projections Frontiers in Plant Science rice system extreme climate indices multi-model ensemble global climate model CMIP6 |
| title | Projected future changes in extreme climate indices affecting rice production in China using a multi-model ensemble of CMIP6 projections |
| title_full | Projected future changes in extreme climate indices affecting rice production in China using a multi-model ensemble of CMIP6 projections |
| title_fullStr | Projected future changes in extreme climate indices affecting rice production in China using a multi-model ensemble of CMIP6 projections |
| title_full_unstemmed | Projected future changes in extreme climate indices affecting rice production in China using a multi-model ensemble of CMIP6 projections |
| title_short | Projected future changes in extreme climate indices affecting rice production in China using a multi-model ensemble of CMIP6 projections |
| title_sort | projected future changes in extreme climate indices affecting rice production in china using a multi model ensemble of cmip6 projections |
| topic | rice system extreme climate indices multi-model ensemble global climate model CMIP6 |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1595367/full |
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