Abstract
Extreme climate events constitute a major risk to global food production. Among these, extreme rainfall is often dismissed from historical analyses and future projections, the impacts and mechanisms of which remain poorly understood. Here we used long-term nationwide observations and multi-level rainfall manipulative experiments to explore the magnitude and mechanisms of extreme rainfall impacts on rice yield in China. We find that rice yield reductions due to extreme rainfall were comparable to those induced by extreme heat over the last two decades, reaching 7.6 ± 0.9% (one standard error) according to nationwide observations and 8.1 ± 1.1% according to the crop model incorporating the mechanisms revealed from manipulative experiments. Extreme rainfall reduces rice yield mainly by limiting nitrogen availability for tillering that lowers per-area effective panicles and by exerting physical disturbance on pollination that declines per-panicle filled grains. Considering these mechanisms, we projected ~8% additional yield reduction due to extreme rainfall under warmer climate by the end of the century. These findings demonstrate that it is critical to account for extreme rainfall in food security assessments.
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Data availability
The data from the national agrometeorological observation network and the rainfall manipulative experiments are available in Supplementary Data 1. The climate data, records of extreme climate events, rice yield and phenology at the site scale from the CMA are available at https://data.cma.cn/en. Model input data for historical simulations and future projections are available from public data depositories listed in Supplementary Table 5. The GPM IMERGv6 are available at https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGHH_06/summary. Model output data for historical simulations and future projections are available in Supplementary Data 2. Source data are provided with this paper.
Code availability
Source codes for data analyses are available from https://doi.org/10.6084/m9.figshare.19801765. Source codes for process-based model are available from http://forge.ipsl.jussieu.fr/orchidee, under the French Free Software license, compatible with the GNU GPL (http://cecill.info/licences.en.html).
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Acknowledgements
This study was supported by the National Natural Science Foundation of China (42225102 and 41977082, F.Z.; 42007079, J.F.; 42171096, X.W.; 41530528, S.L.P). We acknowledge K. Liu, J. Wang and L. Shu from Jingzhou Agrometeorological Experimental Station, S. Wang, W. Adalibieke, Y. Bo, C. Wu, W. Jiang, M. Yuan, H. Cai and C. Wang from Peking University, X. Huang, L. Chen and D. Zhuang from Central China Normal University and C. Li from South China Sea Institute of Oceanography, Chinese Academic of Science for supporting field experiments and laboratory analyses. We acknowledge the CMA for nationwide observations of rice yield, phenology, hourly precipitation and extreme climate events, and the NASA for GPM IMERGv6 data.
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F.Z. designed the study. Y.J., J.F. and X.W. performed all computational analyses. F.Z., X.W., J.F. and Y.J. drafted the paper. L.L. provided high-resolution climate projection using the IPSL model. All co-authors reviewed and commented on the paper.
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Nature Food thanks David Makowski, Yan Li and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Supplementary Texts 1 and 2, Figs. 1–23, Tables 1–5 and Data 1 and 2.
Supplementary Data 1
Long-term nationwide observations and multi-level rainfall manipulative experiments dataset.
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Model output data.
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Fu, J., Jian, Y., Wang, X. et al. Extreme rainfall reduces one-twelfth of China’s rice yield over the last two decades. Nat Food 4, 416–426 (2023). https://doi.org/10.1038/s43016-023-00753-6
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DOI: https://doi.org/10.1038/s43016-023-00753-6
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