Pooled Prediction of the Individual and Combined Impact of Extreme Climate Events on Crop Yields in China
Abstract
1. Introduction
2. Materials and Methods
2.1. Data Sources
2.2. Definition of Historical Crop Yield Reduction Rates
2.3. Definition of Frequency and Intensity of Four ECEs
2.4. Selection of the Most Suitable Model for the Impact of ECEs on Crop Yields
2.5. Pooled Prediction of the Impact of Four ECEs on Crop Yields in China
3. Results
3.1. Optimal Modeling of the Impact of Different Extreme Climate Events on Crop Yields in Different Regions
3.2. Relative Changes in Crop Yields Due to Individual Impacts of Extreme Climate Events
3.2.1. Relative Changes in Crop Yields Under the Individual Impacts of Drought
3.2.2. Relative Changes in Crop Yields Under the Individual Impacts of Extreme Precipitation
3.2.3. Relative Changes in Crop Yields Under the Individual Impacts of Extreme Low Temperature and Extreme Wind
3.3. Estimation of the Combined Impact of Extreme Climate Events on the Crop Yield Change
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Fu, J.; Jian, Y.; Wang, X.; Li, L.; Ciais, P.; Zscheischler, J.; Wang, Y.; Tang, Y.; Müller, C.; Webber, H.; et al. Extreme rainfall reduces one-twelfth of China’s rice yield over the last two decades. Nat. Food 2023, 4, 416–426. [Google Scholar] [CrossRef]
- Lacoste, M.; Cook, S.; McNee, M.; Gale, D.; Ingram, J.; Bellon-Maurel, V.; MacMillan, T.; Sylvester-Bradley, R.; Kindred, D.; Bramley, R.; et al. On-farm Experimentation to transform global agriculture. Nat. Food 2022, 3, 11–18. [Google Scholar] [CrossRef]
- Shi, W.; Wang, M.; Liu, Y. Crop yield and production responses to climate disasters in China. Sci. Total Environ. 2021, 750, 141147. [Google Scholar] [CrossRef] [PubMed]
- Xu, Q.; Liang, H.; Wei, Z.; Zhang, Y.; Lu, X.; Li, F.; Wei, N.; Zhang, S.; Yuan, H.; Liu, S.; et al. Assessing climate change impacts on crop yields and exploring adaptation strategies in northeast China. Earth’s Future 2024, 12, e2023EF004063. [Google Scholar] [CrossRef]
- Vogel, E.; Donat, M.G.; Alexander, L.V.; Meinshausen, M.; Ray, D.K.; Karoly, D.; Meinshausen, N.; Frieler, K. The effects of climate extremes on global agricultural yields. Environ. Res. Lett. 2019, 14, 054010. [Google Scholar] [CrossRef]
- Hasegawa, T.; Sakurai, G.; Fujimori, S.; Takahashi, K.; Hijioka, Y.; Masui, T. Extreme climate events increase risk of global food insecurity and adaptation needs. Nat. Food 2021, 2, 587–595. [Google Scholar] [CrossRef] [PubMed]
- Liu, Y.; Chen, J.; Pan, T.; Liu, Y.; Zhang, Y.; Ge, Q.; Ciais, P.; Penuelas, J. Global socioeconomic risk of precipitation extremes under climate change. Earth’s Future 2020, 8, e2019EF001331. [Google Scholar] [CrossRef]
- Kornhuber, K.; Lesk, C.; Schleussner, C.F.; Jägermeyr, J.; Pfleiderer, P.; Horton, R.M. Risks of synchronized low yields are underestimated in climate and crop model projections. Nat. Commun. 2023, 14, 3528. [Google Scholar] [CrossRef]
- Lesk, C.; Rowhani, P.; Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 2016, 529, 84–87. [Google Scholar] [CrossRef]
- van Dijk, M.; Morley, T.; Rau, M.L.; Saghai, Y. A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050. Nat. Food 2021, 2, 494–501. [Google Scholar] [CrossRef] [PubMed]
- Abramoff, R.Z.; Ciais, P.; Zhu, P.; Hasegawa, T.; Wakatsuki, H.; Makowski, D. Adaptation strategies strongly reduce the future impacts of climate change on simulated crop yields. Earth’s Future 2023, 11, e2022EF003190. [Google Scholar] [CrossRef]
- Rezaei, E.E.; Webber, H.; Asseng, S.; Boote, K.; Durand, J.L.; Ewert, F.; Martre, P.; MacCarthy, D.S. Climate change impacts on crop yields. Nat. Rev. Earth Environ. 2023, 4, 831–846. [Google Scholar] [CrossRef]
- Asseng, S.; Ewert, F.; Rosenzweig, C.; Jones, J.W.; Hatfield, J.L.; Ruane, A.C.; Boote, K.J.; Thorburn, P.J.; Rötter, R.P.; Cammarano, D.; et al. Uncertainty in simulating wheat yields under climate change. Nat. Clim. Change 2013, 3, 827–832. [Google Scholar] [CrossRef]
- Lobell, D.B.; Asseng, S. Comparing estimates of climate change impacts from process-based and statistical crop models. Environ. Res. Lett. 2017, 12, 015001. [Google Scholar] [CrossRef]
- Yin, X.; Leng, G. Observational constraint of process crop models suggests higher risks for global maize yield under climate change. Environ. Res. Lett. 2022, 17, 074023. [Google Scholar] [CrossRef]
- Sun, T.; Hasegawa, T.; Liu, B.; Tang, L.; Liu, L.; Cao, W.; Zhu, Y. Current rice models underestimate yield losses from short-term heat stresses. Global Change Biol. 2021, 27, 402–416. [Google Scholar] [CrossRef] [PubMed]
- Xiao, L.; Liu, B.; Zhang, H.; Gu, J.; Fu, T.; Asseng, S.; Liu, L.; Tang, L.; Cao, W.; Zhu, Y. Modeling the response of winter wheat phenology to low temperature stress at elongation and booting stages. Agric. For. Meteorol. 2021, 303, 108376. [Google Scholar] [CrossRef]
- Mo, F.; Sun, M.; Liu, X.-Y.; Wang, J.-Y.; Zhang, X.-C.; Ma, B.L.; Xiong, Y.-C. Phenological responses of spring wheat and maize to changes in crop management and rising temperatures from 1992 to 2013 across the Loess Plateau. Field Crops Res. 2016, 196, 337–347. [Google Scholar] [CrossRef]
- Ostberg, S.; Schewe, J.; Childers, K.; Frieler, K. Changes in crop yields and their variability at different levels of global warming. Earth Syst. Dynam. 2018, 9, 479–496. [Google Scholar] [CrossRef]
- Guo, Y.; Fu, Y.; Hao, F.; Zhang, X.; Wu, W.; Jin, X.; Robin Bryant, C.; Senthilnath, J. Integrated phenology and climate in rice yields prediction using machine learning methods. Ecol. Indic. 2021, 120, 106935. [Google Scholar] [CrossRef]
- Leng, G.; Hall, J.W. Predicting spatial and temporal variability in crop yields: An inter-comparison of machine learning, regression and process-based models. Environ. Res. Lett. 2020, 15, 044027. [Google Scholar] [CrossRef]
- Ruane, A.C.; Phillips, M.; Jägermeyr, J.; Müller, C. Non-linear climate change impacts on crop yields may mislead stakeholders. Earth’s Future 2024, 12, e2023EF003842. [Google Scholar] [CrossRef]
- Ray, D.K.; Gerber, J.S.; MacDonald, G.K.; West, P.C. Climate variation explains a third of global crop yield variability. Nat. Commun. 2015, 6, 5989. [Google Scholar] [CrossRef]
- Zhang, E.; Liu, Y.; Zhang, J.; Shi, Z.; Huang, S.; Liu, J.; Pan, T. Uncovering the large increases of thermal units during crop phenological phases in China since 1981. Eur. J. Agron. 2025, 170, 127721. [Google Scholar] [CrossRef]
- Piao, S.; Ciais, P.; Huang, Y.; Shen, Z.; Peng, S.; Li, J.; Zhou, L.; Liu, H.; Ma, Y.; Ding, Y.; et al. The impacts of climate change on water resources and agriculture in China. Nature 2010, 467, 43–51. [Google Scholar] [CrossRef]
- Jägermeyr, J.; Müller, C.; Ruane, A.C.; Elliott, J.; Balkovic, J.; Castillo, O.; Faye, B.; Foster, I.; Folberth, C.; Franke, J.A.; et al. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat. Food 2021, 2, 873–885. [Google Scholar] [CrossRef]
- Zhu, H.; Jiang, Z.; Li, L. Projection of climate extremes in China, an incremental exercise from CMIP5 to CMIP6. Sci. Bull. 2021, 66, 2528–2537. [Google Scholar] [CrossRef] [PubMed]
- Chen, H.; Sun, J.; Lin, W.; Xu, H. Comparison of CMIP6 and CMIP5 models in simulating climate extremes. Sci. Bull. 2020, 65, 1415–1418. [Google Scholar] [CrossRef] [PubMed]
- Grose, M.R.; Narsey, S.; Delage, F.P.; Dowdy, A.J.; Bador, M.; Boschat, G.; Chung, C.; Kajtar, J.B.; Rauniyar, S.; Freund, M.B.; et al. Insights from CMIP6 for Australia’s future climate. Earth’s Future 2020, 8, e2019EF001469. [Google Scholar] [CrossRef]
- Cannon, A.J. Multivariate quantile mapping bias correction: An N-dimensional probability density function transform for climate model simulations of multiple variables. Clim. Dyn. 2018, 50, 31–49. [Google Scholar] [CrossRef]
- Lange, S. Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0). Geosci. Model Dev. 2019, 12, 3055–3070. [Google Scholar] [CrossRef]
- Chen, F.; Jia, H.; Pan, D. Risk Assessment of Maize Drought in China Based on Physical Vulnerability. J. Food Qual. 2019, 2019, 9392769. [Google Scholar] [CrossRef]
- Yue, Y.; Wang, L.; Li, J.; Zhu, A.x. An EPIC model-based wheat drought risk assessment using new climate scenarios in China. Clim. Change 2018, 147, 539–553. [Google Scholar] [CrossRef]
- Shi, W.; Tao, F. Spatio-temporal distributions of climate disasters and the response of wheat yields in China from 1983 to 2008. Nat. Hazard. 2014, 74, 569–583. [Google Scholar] [CrossRef]
- Simelton, E. Food self-sufficiency and natural hazards in China. Food Secur. 2011, 3, 35–52. [Google Scholar] [CrossRef]
- Lesk, C.; Anderson, W.; Rigden, A.; Coast, O.; Jägermeyr, J.; McDermid, S.; Davis, K.F.; Konar, M. Compound heat and moisture extreme impacts on global crop yields under climate change. Nat. Rev. Earth Environ. 2022, 3, 872–889. [Google Scholar] [CrossRef]
- Zscheischler, J.; Martius, O.; Westra, S.; Bevacqua, E.; Raymond, C.; Horton, R.M.; van den Hurk, B.; AghaKouchak, A.; Jézéquel, A.; Mahecha, M.D.; et al. A typology of compound weather and climate events. Nat. Rev. Earth Environ. 2020, 1, 333–347. [Google Scholar] [CrossRef]
- Zhou, Z.; Zhang, L.; Chen, J.; She, D.; Wang, G.; Zhang, Q.; Xia, J.; Zhang, Y. Projecting global grought risk under various SSP-RCP scenarios. Earth’s Future 2023, 11, e2022EF003420. [Google Scholar] [CrossRef]
- Vicente-Serrano, S.M.; Beguería, S.; López-Moreno, J.I. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J. Clim. 2010, 23, 1696–1718. [Google Scholar] [CrossRef]
- Wei, X.; He, W.; Zhou, Y.; Cheng, N.; Xiao, J.; Bi, W.; Liu, Y.; Sun, S.; Ju, W. Increased sensitivity of global vegetation productivity to drought over the recent three decades. J. Geophys. Res. Atmos. 2023, 128, e2022JD037504. [Google Scholar] [CrossRef]
- He, Q.; Wang, M.; Liu, K.; Wang, B. High-resolution Standardized Precipitation Evapotranspiration Index (SPEI) reveals trends in drought and vegetation water availability in China. Geogr. Sustain. 2025, 6, 100228. [Google Scholar] [CrossRef]
- Wei, T.; Liu, C.; Dong, W.; Yu, H.; Yang, S.; Yan, Q.; Hao, Z. Reduction in extreme climate events and potential impacts by the use of technological advances. Int. J. Climatol. 2021, 41, 2495–2508. [Google Scholar] [CrossRef]
- Leng, G.; Hall, J. Crop yield sensitivity of global major agricultural countries to droughts and the projected changes in the future. Sci. Total Environ. 2019, 654, 811–821. [Google Scholar] [CrossRef] [PubMed]
- Akaike, H. Likelihood of a model and information criteria. J. Econom. 1981, 16, 3–14. [Google Scholar] [CrossRef]
- Schwarz, G. Estimating the dimension of a model. Ann. Stat. 1978, 6, 461–464. [Google Scholar] [CrossRef]
- Ciscar, J.-C.; Iglesias, A.; Feyen, L.; Szabó, L.; Van Regemorter, D.; Amelung, B.; Nicholls, R.; Watkiss, P.; Christensen, O.B.; Dankers, R.; et al. Physical and economic consequences of climate change in Europe. Proc. Natl. Acad. Sci. USA 2011, 108, 2678–2683. [Google Scholar] [CrossRef]
- Zhou, T.; Yu, R. Twentieth-century surface air temperature over China and the globe simulated by coupled climate models. J. Clim. 2006, 19, 5843–5858. [Google Scholar] [CrossRef]
- Fahad, S.; Bajwa, A.A.; Nazir, U.; Anjum, S.A.; Farooq, A.; Zohaib, A.; Sadia, S.; Nasim, W.; Adkins, S.; Saud, S.; et al. Crop production under drought and heat stress: Plant responses and management options. Front. Plant Sci. 2017, 8, 1147. [Google Scholar] [CrossRef]
- Telfer, P.; Edwards, J.; Bennett, D.; Ganesalingam, D.; Able, J.; Kuchel, H. A field and controlled environment evaluation of wheat (Triticum aestivum) adaptation to heat stress. Field Crops Res. 2018, 229, 55–65. [Google Scholar] [CrossRef]
- Dubey, R.; Pathak, H.; Chakrabarti, B.; Singh, S.; Gupta, D.K.; Harit, R.C. Impact of terminal heat stress on wheat yield in India and options for adaptation. Agric. Syst. 2020, 181, 102826. [Google Scholar] [CrossRef]
- Liu, Y.; Zhang, E.; Pan, T.; Chen, J.; Gao, Y.; Peñuelas, J. Anthropogenic adaptation measures expand suitable area for highland barley in Tibetan Plateau. Sci. Bull. 2025, 70, 1504–1512. [Google Scholar] [CrossRef] [PubMed]
- Ahmad, S.; Abbas, G.; Ahmed, M.; Fatima, Z.; Anjum, M.A.; Rasul, G.; Khan, M.A.; Hoogenboom, G. Climate warming and management impact on the change of phenology of the rice-wheat cropping system in Punjab, Pakistan. Field Crops Res. 2019, 230, 46–61. [Google Scholar] [CrossRef]
- Battisti, R.; Sentelhas, P.C.; Parker, P.S.; Nendel, C.; Câmara, G.M.D.S.; Farias, J.R.B.; Basso, C.J. Assessment of crop-management strategies to improve soybean resilience to climate change in Southern Brazil. Crop Pasture Sci. 2018, 69, 154–162. [Google Scholar] [CrossRef]
- Zhang, E.; Liu, Y.; Tan, Q.; Chen, J.; Pan, T. Projected changes in future wheat yield across China under CMIP6 climate simulations and agricultural management scenarios. J. Cleaner Prod. 2025, 145570. [Google Scholar] [CrossRef]
- Feng, S.; Hao, Z. Quantifying likelihoods of extreme occurrences causing maize yield reduction at the global scale. Sci. Total Environ. 2020, 704, 135250. [Google Scholar] [CrossRef]
- Zhao, H.; Zhang, L.; Kirkham, M.B. U.S. winter wheat yield loss attributed to compound hot-dry-windy events. Nat. Commun. 2022, 13, 7233. [Google Scholar] [CrossRef] [PubMed]








| Model Type | Model Construction |
|---|---|
| 1 | ∆Y ~ fre |
| 2 | ∆Y ~ fre2 |
| 3 | ∆Y ~ ins |
| 4 | ∆Y ~ ins2 |
| 5 | ∆Y ~ fre × ins |
| 6 | ∆Y ~ fre × ins + fre/ins |
| 7 | ∆Y ~ fre2 + ins2 |
| 8 | ∆Y ~ fre2 + ins2 + fre + ins + fre/ins |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Liu, J.; Liu, Y.; Chen, J.; Shi, Z.; Huang, S.; Zhang, E.; Pan, T. Pooled Prediction of the Individual and Combined Impact of Extreme Climate Events on Crop Yields in China. Agronomy 2025, 15, 2319. https://doi.org/10.3390/agronomy15102319
Liu J, Liu Y, Chen J, Shi Z, Huang S, Zhang E, Pan T. Pooled Prediction of the Individual and Combined Impact of Extreme Climate Events on Crop Yields in China. Agronomy. 2025; 15(10):2319. https://doi.org/10.3390/agronomy15102319
Chicago/Turabian StyleLiu, Junjie, Yujie Liu, Jie Chen, Zhaoyang Shi, Shuyuan Huang, Ermei Zhang, and Tao Pan. 2025. "Pooled Prediction of the Individual and Combined Impact of Extreme Climate Events on Crop Yields in China" Agronomy 15, no. 10: 2319. https://doi.org/10.3390/agronomy15102319
APA StyleLiu, J., Liu, Y., Chen, J., Shi, Z., Huang, S., Zhang, E., & Pan, T. (2025). Pooled Prediction of the Individual and Combined Impact of Extreme Climate Events on Crop Yields in China. Agronomy, 15(10), 2319. https://doi.org/10.3390/agronomy15102319

