Weather and Climate Extremes Drive Grain Yield Reductions and Economic Losses in China

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Earlier studies focused narrowly on a single crop type, extreme event type, or small region. This study provides the first comprehensive analysis of heatwaves, drought, extreme cold, and extreme rainfall on wheat, maize, and rice yields in China from 1970 to 2019. We find these extremes reduce grain yields by 3.3, 7.3, 1.9, and 3.0%, respectively, amounting to 33.5, 66.6, 18.0, and 29.3% of the interannual yield variability. Chinese agriculture faces distinct vulnerabilities, with more diverse extremes significantly affecting more crop types compared to global, European, and American assessments. These yield reductions cause annual losses of 9.1 million tons in production and USD 3.1 billion, both increasing over time. Rainfed croplands suffer twice the yield losses of irrigated croplands, suggesting irrigation as an effective adaptation strategy. Current crop models largely underestimate these losses, indicating future climate impacts may be more severe than previously assessed. Earth and environmental sciences/Natural hazards Earth and environmental sciences/Climate sciences/Climate change/Climate and Earth system modelling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Main Weather extremes (e.g., heatwaves, extreme cold, and extreme rainfall) and climate extremes (e.g., drought) critically affect crop yields, contributing 18–43% of the global interannual yield variability 1 . The frequency and intensity of these extremes have increased in recent decades, and are projected to further intensify under climate change 2 , 3 , posing growing threats to global food security and grain market stability 4 . As the world's largest grain producer feeding approximately 20% of the global population 5 , China is particularly vulnerable to these challenges. More than a quarter of China’s total cultivated area is affected annually by various weather and climate extremes 6 . Therefore, quantifying and understanding the impacts of these extremes on China’s crop yields is crucial for developing effective climate change adaptation strategies. The impact of weather and climate extremes on global and European crop yields has been quantified using the Emergency Events Database (EM-DAT, https://www.emdat.be/ ) 7,8 , which collects reported disasters with significant socio-economic influence. However, this dataset includes events occurring outside the crop-growing season or in uncultivated regions, which may not affect crops. Additionally, although several studies have investigated the effects of the extremes on crop yields in China, they are often limited to a specific crop, one single type of extreme event, or a small region during different time periods 9 – 14 . Consequently, it is challenging to compare impacts across crops and extreme event types, hindering our understanding of extreme event impacts on Chinese crop production. In addition to yield loss, understanding the production and corresponding economic losses from weather and climate extremes is also crucial for assessing agricultural risk, yet such losses in China remain unclear. Global databases such as EM-DAT and DesInventar ( https://www.desinventar.net/DesInventar/index.jsp ) and provide total socio-economic losses from each extreme event across different sectors but lack sector-specific breakdowns. FAO reports offer some insight into agricultural disaster losses, but only on a global and continental scale 15 . Global crop production losses due to extremes have been estimated in several studies 7 , 16 , 17 , with only just a few including quantitative assessments for China as part of their global analysis, each specifically examining the impacts of either drought or flood 16 , 17 . Global Gridded Crop Models (GGCMs) provide insightful datasets for understanding the future impacts of climate changes and weather and climate extremes on global and regional crop yields 4 , 18 – 20 , as they simulate crop growth processes and their responses to environmental, climate, and management factors. The Global Gridded Crop Model Intercomparison (GGCMI) is an international initiative to improve process-based crop modeling and to develop multi-model ensemble assessments under consistent protocols and inputs 20 . Several studies have assessed the ability of GGCMs to model responses to heatwaves, drought, and/or extreme rainfall on a global scale, as well as in the USA and Europe 21 – 25 , but the effects of extreme cold events remain unknown. Furthermore, while the capability of GGCMs to simulate the impact of excessive wetness on maize has been evaluated in China 26 , the effects on other crops, as well as the impacts of heatwaves, drought, and extreme cold on crops, remain unexplored. To address these issues, this study quantified the impacts of weather and climate extremes (heatwaves, drought, extreme cold, and extreme rainfall) on the yields of the three major grain crops (wheat, maize, and rice) at the provincial scale in China from 1970 to 2019. The analysis applied the superposed epoch analysis (SEA) method to quantify the impacts, by using multiple data sources, including crop yield statistics, harvested area distributions, satellite-based crop phenology data, and meteorological observations. Only the extremes occurring in cultivated regions and during the growing season of specific crops were considered. Additionally, we used meteorological definitions of extremes (Methods), allowing future impact projection and direct comparisons between historical and future estimates. We further quantified the differential influences of extreme events on rainfed and irrigated croplands to determine whether irrigation mitigates the negative impacts of these extremes. Based on the quantified yield loss, we estimated the corresponding production and economic losses for the period from 1991 to 2019 when data on agricultural product prices were available. Finally, the performance of 23 GGCMs participating in GGCMI phases 1 27,28 and 3 20 was evaluated in simulating the influences of extreme events in China using historical simulations from 1980 to 2010, providing guidance for the future development and application of GGCMs. Results Occurrence frequencies of weather and climate extremes Among the crops, wheat experienced the least frequency of weather and climate extremes, with a total of 237 province-level extreme events from 1970 to 2019, dominated by extreme cold (Fig. 1 a; Table S1 ). Rice experienced the most frequent extreme events, six times more than wheat and twice as many as maize. Maize and rice were most often affected by extreme rainfall (257 and 507 instances, respectively) and heatwaves (226 and 491 instances). For both crops, extreme cold was the next most frequent occurrence (164 and 316 instances), while drought was the least common (121 and 108 instances) (Fig. 1 b and c; Table S1 ). Furthermore, from 1970 to 2019, the frequency of heatwaves affecting maize and rice, drought impacting maize, and extreme rainfall affecting rice increased significantly (Fig. S1 ). In contrast, the incidence of extreme cold has significantly decreased for all three crops (Fig. S1 ). Influences of weather and climate extremes on yields We employed the superposed epoch analysis, a compositing method that isolates the average response of particular events 7 (Methods), to quantify the influences of heatwaves, drought, extreme cold, and extreme rainfall at the provincial scale in China from 1970 to 2019. The results showed that all those extreme events induced significant (P < 0.05) grain yield losses in China (Fig. 2 ). Drought caused the strongest yield losses (7.3%), more than two times the losses from heatwaves (3.3%), extreme rainfall (3.0%), and extreme cold (1.9%) (Fig. 2 ). We then compared the yield losses caused by extreme events with the yield interannual variation. The yield losses from heatwaves, drought, extreme cold, and extreme rainfall amounted to 33.5%, 66.6%, 18.0%, and 29.3% of the interannual variation, respectively (Table S2). The impacts of different weather and climate extremes significantly vary across crop types. Wheat and maize were most sensitive to drought, with the highest yield losses of 9.6% and 9.9%, respectively (Fig. 3 c and Table S3). Maize was also strongly affected by heatwaves and extreme rainfall, showing significant losses of 6.0% and 4.7% (Fig. 3 and Table S3). Rice, on the other hand, was sensitive to heatwaves, extreme cold, and extreme rainfall, with smaller yield losses of 1.7%, 3.4%, and 1.9% (Fig. 3 and Table S3). These reductions amounted to a significant 38.2–83.4% of the interannual variation, except for the rice yield losses from heatwaves and extreme rainfall, which accounted for 21.5% and 21.0% (Table S3). Further analysis of the differential influences of weather and climate extremes on rainfed and irrigated croplands revealed that irrigation reduced yield losses from heatwaves, drought, and extreme rainfall by approximately 50% (Fig. 4 ). These extremes caused significant yield reductions on both rainfed and irrigated croplands, but with different magnitudes. Specifically, rainfed croplands experienced yield losses of 6.4% from heatwaves, 11.1% from drought, and 4.3% from extreme rainfall, while irrigated croplands showed lower losses of 2.3%, 5.7%, and 2.4%, respectively. The effect of irrigation was small for extreme cold. Production and economic losses The four extreme event types collectively led to an average grain production loss of 9.1 million tons per year, ranging from 3.0 to 17.5 million tons per year during the period of 1991–2019, culminating in a loss of 264.9 million tons over the period (Fig. 5 a and Table S4). This translates to an annual economic loss of USD 3.1 billion, resulting in a total loss of USD 90.8 billion over the period (Fig. 5 b and Table S4). Even though drought caused the largest yield loss, heatwaves resulted in the most substantial production losses among the four types of extreme events (Table S4), primarily due to their higher frequency (Table S5), leading to a production loss of 88.0 million tons and a corresponding economic loss of USD 32.6 billion, contributing to 33.2% and 35.9% of total losses, respectively (Fig. 5 and Table S4). Most losses due to heatwaves occurred in the last decade (Fig. 5 ). In contrast, extreme cold caused the smallest losses, particularly in the past decade, with a production loss of 39.0 million tons and an economic loss of USD 12.8 billion (Fig. 5 and Table S4). Drought related losses were concentrated in the 2000s, whereas extreme rainfall led to losses mainly in the other years (Fig. 5 ). Considering different crop types, maize showed the highest sensitivity, leading to the largest losses among the grain crops, accounting for 51.4% of total production losses and 49.1% of the economic losses (Figs. S2–4). Although rice was the most frequently impacted crop by extremes (Tables S5), the resulting damage was smaller than that for maize (Figs. S3). For wheat, fewer extreme events occurred (Table S5), with sensitivity primarily to drought, resulting in cumulative production and economic losses of only 4.3% and 2.9% of the total losses, respectively (Fig. 4 and Fig. S4). From 1991 to 2019, production losses due to extreme events increased significantly (P < 0.05), with a trend of 0.15 million tons yr − 1 (Fig. 5 a). This was primarily driven by the yield rise for all grain crops and the expansion of maize’s harvested area, along with the increased frequency of heatwaves affecting maize and rice (Figs. S1 and Fig. S5a–f). Moreover, the economic losses exhibited an even more significant increase (P < 0.01) of 0.13 million 2019 USD yr − 1 (Fig. 5 b), driven by rising agricultural producer prices (Fig. S5g–i). In the last decade, the average annual production and economic losses were up to 10.5 million tons and USD 4.6 billion (Table S6). Evaluation of GGCMs GGCMs generally underestimated the yield losses (Fig. 6 and Figs. S6 − 12), performing best for maize, consistent with previous evaluation study indicating that GGCMs best simulated the interannual maize yield variability 28 . Specifically, in GGCMI phase 1, four, ten, and seven out of the twelve models, and in GGCMI phase 3, four, eight, and eight out of the eleven models, captured the significant yield losses caused by heatwaves, drought, and extreme rainfall, respectively. However, nearly all models underestimated maize yield losses, highlighting a consistent bias in the model outputs. Wheat yield losses were similarly underestimated. The models performed worst in reproducing rice's response to extremes. In phase 1, no model could capture the rice yield loss, and most models even incorrectly predicted yield increases during extreme events, contradicting observed yield decreases. Phase 3 models, compared to their phase 1 counterparts, showed improved performance in capturing the effects of heatwaves and extreme rainfall on rice. Nevertheless, they continued to struggle with simulating the impact of extreme cold (Fig. 6 ). Discussion Our results not only confirm previous findings 9 , 10 that drought significantly reduced China's wheat yield and that maize yield declined due to water extremes (Fig. 3 c, d), but also reveal new insights through our comprehensive analysis of multiple extreme events and crop types. Specifically, we found that China's maize yield was also sensitive to heatwaves (Fig. 3 a), similar to findings for the USA 29 . Rice yield was primarily influenced by heatwaves, extreme cold, and extreme rainfall (Fig. 3 ). While a previous study indicated that extreme rainfall led to the greatest rice yield loss in China 14 , we found that extreme cold resulted in the highest losses. This discrepancy likely stems from differences in defining extremes: we used meteorological thresholds (Methods), whereas the study 14 defined extremes based on known physiological impacts which are uncertain and therefore unsuitable for informing future impact projections. The impacts of weather and climate extremes on crop yields in China differ from global and European estimates based on the EM-DAT database. While our findings corroborated the global-scale study’s conclusion 7 that heatwaves and drought significantly reduced grain yield in China (Fig. 2 a, c), we identified notable differences in the magnitude of these effects. Drought in China led to a larger yield loss of 7.3%, while heatwaves caused a smaller loss of 3.3%, in contrast to the global estimates of 5.1% and 7.6% 7 , respectively. Compared to analysis in Europe, the impacts of drought and heatwaves in China were less severe (9.0% and 7.3% in Europe, respectively), while extreme cold had a stronger effect in China than in Europe (1.9%vs 1.3% 8 ). More importantly, our findings revealed that, compared to results at the global scale and in Europe and America, a greater variety of extreme events shows statistically significant impacts on more crop types in China. Globally, maize was the only cereal crop significantly affected by heatwaves and drought 7 , whereas in China, wheat also showed high vulnerability to drought and rice to heatwaves. In addition, we identified significant yield losses due to extreme cold (1.9%) and extreme rainfall (3.0%) (Fig. 2 b, d), impacts that were not observed as significant in the global-scale analysis 7 . Extreme rainfall significantly affected yields in China but had little impact in Europe 8 . Furthermore, rice exhibited little sensitivity to extreme events in America 30 , while it was significantly affected by heatwaves, extreme cold, and extreme rainfall in China. We found that irrigation can buffer the influence of heatwaves, drought, and extreme rainfall on crop yields in China (Fig. 4 ), aligning with findings in America 30 . Irrigation alleviates crop water stress and reduces canopy temperature through enhanced evaporative cooling, thus mitigating the adverse effects of heatwaves and drought 31 , 32 . Additionally, regions with well-developed irrigation infrastructure often adopt better land management practices, such as drainage systems and the use of flood-tolerant varieties 33 , which may explain the smaller yield losses in irrigated croplands during extreme rainfall events (Fig. 4 d). Our study provides comprehensive estimates of crop production and corresponding economic losses due to various weather and climate extremes in China. Our estimates show that these extremes resulted in an average annual production loss of 9.1 million tons, associated with an average economic loss of USD 3.1 billion (Fig. 5 ). The production loss contributed 13% of global grain production losses reported by FAO for all disasters 15 , with Chinese heatwaves and drought alone causing annual declines of 3.0 million tons (11.1% of global losses) and 2.2 million tons (5.3% of global losses), respectively 7 (Table S4). The economic impact is equivalent to 2.5% of global economic losses in grain, cash, and energy crops as well as livestocks 15 and 11% of China's grain import value 5 . Even more concerning, the production and economic losses due to extremes in China have significantly increased over the past three decades. These numbers highlight China's critical role in global agricultural vulnerability and its growing implications for food security and international trade. Our evaluation of 23 GGCMI crop models revealed their general underestimation of weather and climate extreme impacts for China, with some models even failing to capture yield reductions. This suggests that GGCM-based future yield projections may be overly optimistic, particularly concerning the projected rising frequency of heatwaves, drought, and extreme rainfall events 2 , 3 . GGCMI phase 3 models show improvements over their phase 1 predecessors in simulating the effects of heatwaves and extreme rainfall on rice yields. These improvements are likely attributed to developments in model and input data, such as more accurate input data of planting and harvest dates 20 . Nonetheless, significant challenges remain in the current modeling of extreme weather and climate stresses. Many GGCMs account for the impacts of heatwaves and extreme cold on growing season length (through affecting growing degree-day) and photosynthesis, and the impacts of drought on photosynthesis or leaf senescence (Table S7). However, most of them do not explicitly model the effects of heatwaves, drought, and extreme cold during the reproductive period when crop yields are most vulnerable (Supplementary Text 1). For example, heatwaves, drought, and extreme cold can reduce grain number through abortion and sterility, while drought can also shorten the grain-filling period, reducing grain size 34 , 35 . When it comes to extreme rainfall, only a handful of models, specifically CGMS-WOFOST, the EPIC family, ACEA, and CYGMA, partially account for its effects, such as reducing photosynthesis, limiting leaf growth, inducing leaf senescence, or increasing nutrient leaching (Supplementary Text 1). Moreover, some critical mechanisms, such as extreme rainfall reduces effective panicles during the vegetative phase and decreases grain numbers during the reproductive phase for rice 14 , are overlooked. In addition, even among models that do incorporate some of these critical influences, our evaluation suggests they still underestimate the effects. There is a clear need for substantial improvements in parameterization to accurately capture the full impacts of extreme events. While our study offers crucial insights, it is essential to acknowledge several key limitations that suggest directions for future research. First, the sensitivity of crops to extremes varies with phenological stages 34 , 36 . However, we did not differentiate the impacts of extreme events across these stages. Future research should provide a more comprehensive understanding of how extreme events affect crops through their lifecycle. Second, our study does not consider the varying intensities, spatial extent, and durations of extreme events on crop yields. Moreover, our study does not fully address the increasing prevalence of compound climate extremes. Recent research indicates that co-occurring extreme events can synergistically affect crop yields, potentially causing greater impacts than individual events 37 , 38 . This emerging phenomenon highlights the urgent need for comprehensive studies that quantify the complex interactions and cumulative impacts of compound climate extremes on agricultural systems. This study provides a comprehensive quantification of weather and climate extreme impacts on grain yields in China, offering critical estimates of related production and economic losses. Our findings reveal the complex, region-specific nature of climate impacts on agriculture, emphasizing the need for tailored mitigation and adaptation strategies. We identify vulnerable crop types and key extreme events, laying the groundwork for targeted interventions such as crop-specific protection measures, optimized planting schedules, and climate-resilient crop breeding 39 . These insights are crucial for guiding policy decisions, financial investments, and insurance frameworks to enhance agricultural resilience. Furthermore, our evaluation of GGCMs highlights the urgent need to improve the structural representation and parameterization of extreme event stresses, which is critical for refining future crop production projections and food security assessments. Methods Crop data Statistical data on provincial-level grain yields and harvested areas for the three major grain crops (wheat, maize, and rice) from 1970 to 2019 were obtained from the National Bureau of Statistics of China (NBSC, https://data.stats.gov.cn/english ). The linear trend of grain yields was removed, which was mainly related to the development of agricultural management in China 28 . The irrigated and rainfed harvested area distribution data were provided by GGCMI phase 3 20 , based on the MIRCA2000 (Monthly Irrigated and Rainfed Crop Areas around the year 2000) 40 and Siebert et al. 41 , and were held constant over time. The phenological data used to identify the extreme events that happened during the crop growing season, including the start of the vegetative growth period dates (green-up date for winter wheat, emergence date for spring wheat, transplanting date for rice, and V3 stage for maize) and maturity dates, were sourced from ChinaCropPhen1km dataset 42 , which has a spatial resolution of 1 km and covers the period from 2000 to 2019. Note that the overwintering stage of winter wheat was not included in this study. The phenological data of each crop were interpolated and aggregated into provincial scales using the method of area-weighted averaging, and 2000 to 2019 averages were adopted. To estimate the economic losses, agricultural producer price data (USD/ton) for the three crops from 1991 to 2019 (the period for which agricultural producer price data is available) were sourced from the Food and Agriculture Organization of the United Nations (FAO) 5 . Missing years were linearly interpolated between the adjacent values. The prices were converted to their 2019 equivalents using inflation data from the World Bank ( https://data.worldbank.org/ ) to facilitate comparison. Climate data The daily maximum temperature, minimum temperature, and precipitation from 1970 to 2019 were obtained from CN05.1 43 to identify heatwaves, extreme cold, and extreme rainfall events. The CN05.1 data was constructed based on interpolation from observation stations in China, with a spatial resolution of 0.25°. The monthly self-calibrating Palmer Drought Severity Index (scPDSI) from 1970 to 2019, with a spatial resolution of 0.5°, was used to identify drought 44 . To identify weather and climate extremes affecting crops at the provincial level, all climate factors were aggregated into provincial scales using the harvested area distribution data for each crop. Crop models Twelve GGCMs (CGMS-WOFOST, CLM4.5post-crop, EPIC-Boku, EPIC-IIASA, GEPIC, LPJ-GUESS, LPJmL, ORCHIDEE-crop, pAPSIM, pDSSAT, PEGASUS, and PEPIC) from GGCMI phase 1 alongside eleven GGCMs (ACEA, CROVER, CYGMA, EPIC-IIASA, ISAM, LandscapeDNDC, LPJmL, pDSSAT, PEPIC, PROMET, SIMPLACE-LINTUL5) from GGCMI phase 3 were evaluated in this study (Table S7). The GGCMI phase 1 project aimed to understand the skill of GGCM in reproducing observed historic crop yield 27 , 45 , and GGCMI phase 3 also included historical simulations to evaluate GGCM performance 20 . Simulations of the twelve GGCMI phase 1 models were obtained from Müller et al. 45 . Simulations of GGCMI phase 1 under default scenarios, which represent the highest simulation skill of each model, were used. The simulations were forced by the AgMERRA climate dataset for GGCMI phase 1 and by GSWP3-W5E5 (PROMET was forced by WFDE5) for GGCMI phase 3, with a spatial resolution of 0.5° (Table S7). Simulations for the period of 1980–2010 (1981–2009 for PROMET) were used. To ensure the alignment of the gridded GGCMI yield simulations for each growing season with the observed yields at the provincial scale for each calendar year, we first conducted temporal and spatial data preprocessing. Temporally, we aligned the yield of a growing season with the calendar year based on the harvest date calculated based on the actual planting date and days from planting to maturity provided by GGCMI 27 , 28 . Spatially, the simulations were then aggregated into provincial scales by weighting irrigated and rainfed simulated yields with their respective harvested areas. Finally, the provincial yields were detrended. Definitions of weather and climate extremes This study quantified the impacts of four main weather and climate extremes (heatwaves, drought, extreme cold, and extreme rainfall) on crop yields, with definitions of each extreme event provided as follows. For weather extremes, a provincial heatwave event affecting a specific crop was defined as at least three consecutive days with daily maximum temperatures above 35°C in the crop’s planting regions during its growing season, following the definition of the Chinese Meteorological Administration heat warnings 46 . A provincial extreme cold event affecting a specific crop was defined as at least six consecutive days with daily minimum temperatures below the 10th percentile of the minimum temperature distribution in the crop’s planting regions during its growing season, following the definition of the cold spell duration index (CSDI) recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) 47 . A provincial extreme rainfall event affecting a specific crop occurred when at least one day of daily precipitation exceeded 50 mm (38 mm or 25 mm) during the crop’s growing season in its planting regions where the mean annual precipitation was above 400 mm (between 200 mm–400 mm or below 200 mm), following the definition provided by the China meteorological administration 48 . In terms of the climate extreme, according to previous studies 49 , 50 , a provincial drought event affecting a specific crop was identified when the PDSI remained below − 3 for at least one month. For compound events, each type of extreme event was recorded separately. Estimation of crop losses We used the superposed epoch analysis (SEA) method to quantify the average impacts of weather and climate extremes on grain yields. SEA is a statistical method used to enhance the response signal of particular events while reducing background noise from extraneous factors, commonly applied to estimate the impacts of extreme events on crop yields 7 , 8 , 11 , 25 . We defined years in which a specific type of weather and climate extreme affected a particular crop (even if the year co-occurring with other extreme events) as extreme event years for that crop due to the corresponding extreme event. For certain types of weather and climate extremes, we extracted a short time series of yield using a 7-year window, with the extreme event year positioned at the center. For an extreme event occurring in consecutive years, we averaged yield across those years to derive a single yield value affected by the extreme event. Yields before or after the center yield affected by extreme events were excluded from the time series. The time series were normalized relative to the average of the 3 years preceding and following the event to remove the absolute magnitude of provincial data from the signal. The 7-year time series was then centered on the year of extreme and averaged to generate a composited time series. To test the significance of impacts in the SEA, we generated a fictitious event set for each type of weather and climate extreme, with the same sample size as the true event set by randomly selecting year-province combinations from the entire set of years and provinces in which the true events occurred, excluding those included in the true event set. The fictitious event set was analyzed as the true to generate control composites time series. This testifying process was repeated 1,000 times for robustness. The yield was considered a significant deficit or surplus if the true composite in the year of extreme was less than 2.5% or greater than 97.5% of the 1000 control composites, corresponding to a two-tailed 95% confidence level. The deficit or surplus was then quantified by comparing the true composite with the mean of the control composites. We classified provinces into rainfed or irrigated dominated categories if the rainfed or irrigated crop area in the province accounted for more than half of the total crop area based on the harvested area distribution data. The influence of weather and climate extremes on grain yields was quantified separately for rainfed and irrigated dominated provinces to assess the differential influences of extreme events on these two types of croplands. We calculated the national interannual variations of crop yields as the mean coefficient of variation (CV) of detrended provincial yields during non-extreme years. We then compared this with the national average of yield losses. Based on the quantified yield losses, for a given province, we estimated the provincial grain production loss ( PL ) of each crop due to each weather and climate extreme in the year of extreme ( y ) as: $$\:{PL}_{y}={A}_{y}\times\:\frac{{Y}_{y}}{1-YL}\times\:YL$$ 1 Where A y and Y y is the statistical provincial crop harvested area and yield in the year of extreme ( y ), YL (%) is the quantified crop yield loss due to weather and climate extreme averaged over 1970 − 2019 based on the SEA. The use of YL (%) is justified as the losses are overall stable over time, confirmed by Student's t-test comparing the relative reduction of sub-periods (1991 − 2004 and 2005 − 2019) against the relative reduction for 1970 − 2019 for all crop-extreme combinations, except for heatwave impacts on rice during 2005 − 2019 (Fig. S13). The estimated provincial grain production losses were then aggregated to the national total production loss. Subsequently, the national production losses were converted into economic losses based on the agricultural producer price. Declarations Data availability The statistics on provincial-level grain yields and harvested areas are available at https://data.stats.gov.cn/english . The crop phenological data are available at https://doi.org/10.6084/m9.figshare.8313530 . The agricultural producer price data are available at https://www.fao.org/faostat/en/#compare , and the inflation data are at https://data.worldbank.org/ . The climate data are available at https://ccrc.iap.ac.cn/resource/detail?id=228 and https://crudata.uea.ac.uk/cru/data/drought/ . Yield simulations from GGCMI phase 1 crop models are available at https://zenodo.org/ (see Ref. 45 ), and phase 3 yield outputs and harvested area distribution data are available at https://agmip.org/aggrid-ggcmi/ . Code availability All of the python scripts used in the analyses are available from the corresponding author on request. Author Contributions F.L. conceived the research and designed the figures, D.Y. conducted the analyses and produced the figures and tables. J.J. and C.M. provided the GGCMI phases 1 and 3 simulations. D.Y. and F.L. wrote the manuscript with contributions from all authors. Acknowledgements D.Y. and F.L. are supported by the Guangdong Major Project of Basic and Applied Basic Research (Grant No. 2021B0301030007), the National Key Research and Development Program of China (Grant Nos. 2017YFA0604302 and 2017YFA0604804), the National Natural Science Foundation of China (Grant No. 41875137), and the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (EarthLab). L.X. is supported by US National Science Foundation grant AGS-2017113. W.L. was supported by the National Natural Science Foundation of China (Grant No. 52239002 and 52109071). We also acknowledge Toshichika Iizumi, Zhaohui Lin, Zhongda Lin, Xiyan Xu, and Yaqiong Lu for their valuable advice and assistance. References Vogel, E. et al. The effects of climate extremes on global agricultural yields. Environmental Research Letters 14, 054010 (2019). Intergovernmental Panel On Climate Change (Ipcc). 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ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products. Earth Syst. Sci. Data 12, 197–214 (2020). Wu, J. & GAO, X.-J. A gridded daily observation dataset over China region and comparison with the other datasets. Chinese Journal of Geophysics 56, 1102–1111 (2013). Barichivich, J., Osborn, T., Harris, I., van der Schrier, G. & Jones, P. Drought: Monitoring global drought using the self-calibrating Palmer Drought Severity Index. Bulletin of the American Meteorological Society 100, S39–S40 (2019). Müller, C. et al. The global gridded crop model intercomparison phase 1 simulation dataset. Scientific data 6, 50 (2019). You, Q. et al. A comparison of heat wave climatologies and trends in China based on multiple definitions. Climate Dynamics 48, 3975–3989 (2017). ETCCDI. ETCCDI Climate Change Indices. https://etccdi.pacificclimate.org/list_27_indices.shtml (2009). China meteorological administration. Climate index–Waterlogging. https://www.cma.gov.cn/zfxxgk/gknr/flfgbz/bz/202102/t20210210_2720509.html (2020). Sheffield, J., Wood, E. F. & Roderick, M. L. Little change in global drought over the past 60 years. Nature 491, 435–438 (2012). De Luca, P., Messori, G., Wilby, R. L., Mazzoleni, M. & Di Baldassarre, G. Concurrent wet and dry hydrological extremes at the global scale. Earth Syst. Dynam. 11, 251–266 (2020). Additional Declarations There is NO Competing Interest. Supplementary Files Supplementaryinformation.docx Supplementary information for material, tables, and figures Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5629484","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":407925383,"identity":"067e822d-174c-4929-a97c-982c87cf3720","order_by":0,"name":"Fang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACPmbGB0DKhoGBmbmBIYEYLWzMzAZAKg2ohZFYLQxgLYeBGKiFKMDGzsz4ueDX+Wj+dqCWBzV3GPjbDwBF8DuMWXpm3+3cGYdBDjv2jEHiTAKz9Ay8WvgPSPP23M5tAGthA7rwBlCQh4Atv3l7zuXOB2v5d5hBnggtbNI8Pw7kbgBpSWw7zGBAjBZr3obk3I1ALQcS+57xGJ5JbJbGp4Wf/zDzbZ4/drnzzh8++PDHtztycscPH/yMTwsYMLZB6ANAxENkBP2Bsw4QoXoUjIJRMApGGgAARxNHFVRXCcQAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-3686-2257","institution":"Institute of Atmospheric Physics, Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Fang","middleName":"","lastName":"Li","suffix":""},{"id":407925384,"identity":"35cd2b9d-69cc-4878-86c9-13e971d5cac6","order_by":1,"name":"Dezhen Yin","email":"","orcid":"","institution":"Institute of Atmospheric Physics, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Dezhen","middleName":"","lastName":"Yin","suffix":""},{"id":407925385,"identity":"c6acf338-b6bd-4bea-8866-d64b03a9a589","order_by":2,"name":"Jonas Jaegermeyr","email":"","orcid":"https://orcid.org/0000-0002-8368-0018","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Jonas","middleName":"","lastName":"Jaegermeyr","suffix":""},{"id":407925386,"identity":"7028e44e-e479-4a3e-9acc-c51276b306cd","order_by":3,"name":"Christoph Müller","email":"","orcid":"https://orcid.org/0000-0002-9491-3550","institution":"Potsdam Institute for Climate Impact Research","correspondingAuthor":false,"prefix":"","firstName":"Christoph","middleName":"","lastName":"Müller","suffix":""},{"id":407925387,"identity":"0bf5e2db-6273-44e6-b14c-5a508fe8c09b","order_by":4,"name":"Lili Xia","email":"","orcid":"https://orcid.org/0000-0001-7821-9756","institution":"Rutgers University","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Xia","suffix":""},{"id":407925388,"identity":"ca066194-8d7c-424c-a4e2-c966df5575bd","order_by":5,"name":"Florian Zabel","email":"","orcid":"https://orcid.org/0000-0002-2923-4412","institution":"University of Basel","correspondingAuthor":false,"prefix":"","firstName":"Florian","middleName":"","lastName":"Zabel","suffix":""},{"id":407925389,"identity":"6ea039e2-2520-4094-a937-0c8a9e5fc412","order_by":6,"name":"Wenfeng Liu","email":"","orcid":"https://orcid.org/0000-0002-8699-3677","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Wenfeng","middleName":"","lastName":"Liu","suffix":""},{"id":407925390,"identity":"3198fba7-addf-4e41-97f5-9b5ad43bbeb8","order_by":7,"name":"Christian Folberth","email":"","orcid":"https://orcid.org/0000-0002-6738-5238","institution":"International Institute for Applied Systems Analysis","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Folberth","suffix":""},{"id":407925391,"identity":"78af2b05-c106-480d-98cf-d49e62bc8508","order_by":8,"name":"Oleksandr Mialyk","email":"","orcid":"https://orcid.org/0000-0002-7495-2325","institution":"University of Twente","correspondingAuthor":false,"prefix":"","firstName":"Oleksandr","middleName":"","lastName":"Mialyk","suffix":""},{"id":407925392,"identity":"6faf10dd-2b4f-45fb-8d61-37162cd97bab","order_by":9,"name":"Tzu-Shun Lin","email":"","orcid":"https://orcid.org/0000-0002-5741-8585","institution":"NSF National Center for Atmospheric Research","correspondingAuthor":false,"prefix":"","firstName":"Tzu-Shun","middleName":"","lastName":"Lin","suffix":""},{"id":407925393,"identity":"5022b765-7f89-4613-946d-494facf602e3","order_by":10,"name":"Mengxue Li","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Mengxue","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-12-12 07:55:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5629484/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5629484/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75009676,"identity":"5954c182-ec3f-4748-a8c2-182edb33735d","added_by":"auto","created_at":"2025-01-29 11:15:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":166951,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of average grain production and total occurrence frequencies of weather and climate extremes in China during 1970–2019.\u003c/strong\u003e Mean grain production alongside the occurrence frequencies of meteorologically defined weather and climate extremes at the provincial scale for (a) wheat, (b) maize, and (c) rice during 1970–2019. Gray provinces indicate that the crop is not cultivated, or no data is available (e.g., Taiwan). The height of the bars represents the number of extreme events in the 50-year period.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5629484/v1/5637967881f435cbc7bfe797.png"},{"id":75009673,"identity":"3d364bbd-5a3b-44c5-8655-fd2c9fc3a37c","added_by":"auto","created_at":"2025-01-29 11:15:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67759,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eObserved influence of weather and climate extremes on provincial grain yields in China for 1970–2019. \u003c/strong\u003ea–d, Average normalized 7-year time series of yields for wheat, rice, and maize, centered on the year of extremes, for (a) heatwave (n=55), (b) drought (n=57), (c) extreme cold (n=140), and (d) extreme rainfall (n=97). Box plots depict the distributions of 1000 control composites, with crosses denoting extreme outliers. The asterisk denotes the significant yield change (P\u0026lt;0.05) due to the extremes compared to the 1000 control composites, and the number below the asterisk represents the yield change (%) (i.e., the difference between the true composite value and the mean of the 1000 control composites in the years that extremes occur). The observed yields were detrended to exclude management changes over time. Only provinces where crop production accounted for more than 1% of the respective national production were considered.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5629484/v1/fe622e4fafe3ae20fbd22352.png"},{"id":75010497,"identity":"96beb536-13a1-4e08-a735-9b4a15f3cc46","added_by":"auto","created_at":"2025-01-29 11:23:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69832,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eObserved impacts of weather and climate extremes on different crops in China for 1970–2019. \u003c/strong\u003ea–d, Percentage of yield change for wheat (green), maize (orange), and rice (blue) under (a) heatwave, (b) drought, (c) extreme cold, and (d) extreme rainfall, with significant (p \u0026lt; 0.05) yield change marked by asterisks. The number below the asterisk represents the yield change (%). Following the methods used in Fig. 2, yields were detrended, and only provinces where crop production accounted for more than 1% of the respective national production were considered. Besides, results with sample sizes of five or fewer were not presented, e.g., the influences of heatwave (n=5) and extreme rainfall (n=4) on wheat.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5629484/v1/781be54c51404c57fdaeff39.png"},{"id":75010498,"identity":"56bcc966-cf13-454f-b5e3-4fee4d9fd17b","added_by":"auto","created_at":"2025-01-29 11:23:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":21397,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of observed yield changes (%) induced by weather and climate extremes between rainfed and irrigated croplands in China (1970–2019).\u003c/strong\u003e Asterisks indicate statistically significant yield changes (P\u0026lt;0.05), and numbers in parentheses show sample sizes.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5629484/v1/52ff818c9c105ec6bd84b07a.png"},{"id":75009683,"identity":"a658eb39-5144-4260-888a-6e1b6bc268dd","added_by":"auto","created_at":"2025-01-29 11:15:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":34724,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) Annual crop production reductions and (b) corresponding economic losses due to weather and climate extremes in China for 1991–2019. \u003c/strong\u003eThe dashed lines indicate the trend of the losses, showing a significant increase of 0.15 million tons yr\u003csup\u003e−1\u003c/sup\u003e and 0.13 million 2019 USD yr\u003csup\u003e−1\u003c/sup\u003e during 1991–2019 (P\u0026lt;0.05 for production loss and P\u0026lt;0.01 for economic loss). Grain production losses were calculated using provincial crop yield and harvested area data from the NBSC, the years of extremes in each province, and the yield losses detailed in Table S3 (Methods). The corresponding economic losses were then calculated using agricultural producer price data\u003csup\u003e5\u003c/sup\u003e (USD/ton) sourced from the FAO. The values in US dollars were converted to their 2019 equivalents using inflation data sourced from the World Bank (https://data.worldbank.org/) to facilitate comparison. The production and corresponding economic losses were estimated only for 1991 to 2019 due to the availability of agricultural producer price data. Figs. S2−4 shows the production and economic losses for individual crops.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5629484/v1/10babd375499ee2e7cd27f29.png"},{"id":75009682,"identity":"cc8b370c-206a-49ae-ae8d-f33c62c0847b","added_by":"auto","created_at":"2025-01-29 11:15:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":44976,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison between observed and GGCM simulated influence of weather and climate extremes on crop yields in China (1980−2010).\u003c/strong\u003e The analysis focuses on extreme events and crop types with significant observed yield reductions. Bars show yield changes as Fig. 4, and asterisks indicate statistical significance (P\u0026lt;0.05). Rice yield simulations were not available from PEGASUS and pAPSIM. Detailed results by model, crop type, and extreme event are shown in Figs. S6−12.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5629484/v1/ff5be894cb9595fff2865259.png"},{"id":80665213,"identity":"1edc2dbf-7842-4cca-a7f1-6b1cf95d1e8b","added_by":"auto","created_at":"2025-04-15 17:23:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1243969,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5629484/v1/5215f4ea-04c3-401c-a879-dd31e908063c.pdf"},{"id":75009687,"identity":"531750d2-01d6-4f84-838c-7e23ce928259","added_by":"auto","created_at":"2025-01-29 11:15:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8724272,"visible":true,"origin":"","legend":"Supplementary information for material, tables, and figures","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5629484/v1/e5b340d8a35d1818b642fb86.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Weather and Climate Extremes Drive Grain Yield Reductions and Economic Losses in China","fulltext":[{"header":"Main","content":"\u003cp\u003eWeather extremes (e.g., heatwaves, extreme cold, and extreme rainfall) and climate extremes (e.g., drought) critically affect crop yields, contributing 18\u0026ndash;43% of the global interannual yield variability\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The frequency and intensity of these extremes have increased in recent decades, and are projected to further intensify under climate change\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, posing growing threats to global food security and grain market stability\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. As the world's largest grain producer feeding approximately 20% of the global population\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, China is particularly vulnerable to these challenges. More than a quarter of China\u0026rsquo;s total cultivated area is affected annually by various weather and climate extremes\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Therefore, quantifying and understanding the impacts of these extremes on China\u0026rsquo;s crop yields is crucial for developing effective climate change adaptation strategies.\u003c/p\u003e \u003cp\u003eThe impact of weather and climate extremes on global and European crop yields has been quantified using the Emergency Events Database (EM-DAT, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.emdat.be/\u003c/span\u003e\u003cspan address=\"https://www.emdat.be/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e7,8\u003c/sup\u003e, which collects reported disasters with significant socio-economic influence. However, this dataset includes events occurring outside the crop-growing season or in uncultivated regions, which may not affect crops. Additionally, although several studies have investigated the effects of the extremes on crop yields in China, they are often limited to a specific crop, one single type of extreme event, or a small region during different time periods\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Consequently, it is challenging to compare impacts across crops and extreme event types, hindering our understanding of extreme event impacts on Chinese crop production.\u003c/p\u003e \u003cp\u003eIn addition to yield loss, understanding the production and corresponding economic losses from weather and climate extremes is also crucial for assessing agricultural risk, yet such losses in China remain unclear. Global databases such as EM-DAT and DesInventar (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.desinventar.net/DesInventar/index.jsp\u003c/span\u003e\u003cspan address=\"https://www.desinventar.net/DesInventar/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and provide total socio-economic losses from each extreme event across different sectors but lack sector-specific breakdowns. FAO reports offer some insight into agricultural disaster losses, but only on a global and continental scale\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Global crop production losses due to extremes have been estimated in several studies\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, with only just a few including quantitative assessments for China as part of their global analysis, each specifically examining the impacts of either drought or flood\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGlobal Gridded Crop Models (GGCMs) provide insightful datasets for understanding the future impacts of climate changes and weather and climate extremes on global and regional crop yields\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, as they simulate crop growth processes and their responses to environmental, climate, and management factors. The Global Gridded Crop Model Intercomparison (GGCMI) is an international initiative to improve process-based crop modeling and to develop multi-model ensemble assessments under consistent protocols and inputs\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Several studies have assessed the ability of GGCMs to model responses to heatwaves, drought, and/or extreme rainfall on a global scale, as well as in the USA and Europe\u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, but the effects of extreme cold events remain unknown. Furthermore, while the capability of GGCMs to simulate the impact of excessive wetness on maize has been evaluated in China\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, the effects on other crops, as well as the impacts of heatwaves, drought, and extreme cold on crops, remain unexplored.\u003c/p\u003e \u003cp\u003eTo address these issues, this study quantified the impacts of weather and climate extremes (heatwaves, drought, extreme cold, and extreme rainfall) on the yields of the three major grain crops (wheat, maize, and rice) at the provincial scale in China from 1970 to 2019. The analysis applied the superposed epoch analysis (SEA) method to quantify the impacts, by using multiple data sources, including crop yield statistics, harvested area distributions, satellite-based crop phenology data, and meteorological observations. Only the extremes occurring in cultivated regions and during the growing season of specific crops were considered. Additionally, we used meteorological definitions of extremes (Methods), allowing future impact projection and direct comparisons between historical and future estimates. We further quantified the differential influences of extreme events on rainfed and irrigated croplands to determine whether irrigation mitigates the negative impacts of these extremes. Based on the quantified yield loss, we estimated the corresponding production and economic losses for the period from 1991 to 2019 when data on agricultural product prices were available. Finally, the performance of 23 GGCMs participating in GGCMI phases 1\u003csup\u003e27,28\u003c/sup\u003e and 3\u003csup\u003e20\u003c/sup\u003e was evaluated in simulating the influences of extreme events in China using historical simulations from 1980 to 2010, providing guidance for the future development and application of GGCMs.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOccurrence frequencies of weather and climate extremes\u003c/h2\u003e \u003cp\u003eAmong the crops, wheat experienced the least frequency of weather and climate extremes, with a total of 237 province-level extreme events from 1970 to 2019, dominated by extreme cold (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Rice experienced the most frequent extreme events, six times more than wheat and twice as many as maize. Maize and rice were most often affected by extreme rainfall (257 and 507 instances, respectively) and heatwaves (226 and 491 instances). For both crops, extreme cold was the next most frequent occurrence (164 and 316 instances), while drought was the least common (121 and 108 instances) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb and c; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Furthermore, from 1970 to 2019, the frequency of heatwaves affecting maize and rice, drought impacting maize, and extreme rainfall affecting rice increased significantly (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In contrast, the incidence of extreme cold has significantly decreased for all three crops (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInfluences of weather and climate extremes on yields\u003c/h3\u003e\n\u003cp\u003eWe employed the superposed epoch analysis, a compositing method that isolates the average response of particular events\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e (Methods), to quantify the influences of heatwaves, drought, extreme cold, and extreme rainfall at the provincial scale in China from 1970 to 2019. The results showed that all those extreme events induced significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) grain yield losses in China (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Drought caused the strongest yield losses (7.3%), more than two times the losses from heatwaves (3.3%), extreme rainfall (3.0%), and extreme cold (1.9%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We then compared the yield losses caused by extreme events with the yield interannual variation. The yield losses from heatwaves, drought, extreme cold, and extreme rainfall amounted to 33.5%, 66.6%, 18.0%, and 29.3% of the interannual variation, respectively (Table S2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe impacts of different weather and climate extremes significantly vary across crop types. Wheat and maize were most sensitive to drought, with the highest yield losses of 9.6% and 9.9%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and Table S3). Maize was also strongly affected by heatwaves and extreme rainfall, showing significant losses of 6.0% and 4.7% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table S3). Rice, on the other hand, was sensitive to heatwaves, extreme cold, and extreme rainfall, with smaller yield losses of 1.7%, 3.4%, and 1.9% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table S3). These reductions amounted to a significant 38.2\u0026ndash;83.4% of the interannual variation, except for the rice yield losses from heatwaves and extreme rainfall, which accounted for 21.5% and 21.0% (Table S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther analysis of the differential influences of weather and climate extremes on rainfed and irrigated croplands revealed that irrigation reduced yield losses from heatwaves, drought, and extreme rainfall by approximately 50% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These extremes caused significant yield reductions on both rainfed and irrigated croplands, but with different magnitudes. Specifically, rainfed croplands experienced yield losses of 6.4% from heatwaves, 11.1% from drought, and 4.3% from extreme rainfall, while irrigated croplands showed lower losses of 2.3%, 5.7%, and 2.4%, respectively. The effect of irrigation was small for extreme cold.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eProduction and economic losses\u003c/h3\u003e\n\u003cp\u003eThe four extreme event types collectively led to an average grain production loss of 9.1\u0026nbsp;million tons per year, ranging from 3.0 to 17.5\u0026nbsp;million tons per year during the period of 1991\u0026ndash;2019, culminating in a loss of 264.9\u0026nbsp;million tons over the period (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea and Table S4). This translates to an annual economic loss of USD 3.1\u0026nbsp;billion, resulting in a total loss of USD 90.8\u0026nbsp;billion over the period (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb and Table S4).\u003c/p\u003e \u003cp\u003eEven though drought caused the largest yield loss, heatwaves resulted in the most substantial production losses among the four types of extreme events (Table S4), primarily due to their higher frequency (Table S5), leading to a production loss of 88.0\u0026nbsp;million tons and a corresponding economic loss of USD 32.6\u0026nbsp;billion, contributing to 33.2% and 35.9% of total losses, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Table S4). Most losses due to heatwaves occurred in the last decade (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In contrast, extreme cold caused the smallest losses, particularly in the past decade, with a production loss of 39.0\u0026nbsp;million tons and an economic loss of USD 12.8\u0026nbsp;billion (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Table S4). Drought related losses were concentrated in the 2000s, whereas extreme rainfall led to losses mainly in the other years (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsidering different crop types, maize showed the highest sensitivity, leading to the largest losses among the grain crops, accounting for 51.4% of total production losses and 49.1% of the economic losses (Figs. S2\u0026ndash;4). Although rice was the most frequently impacted crop by extremes (Tables S5), the resulting damage was smaller than that for maize (Figs. S3). For wheat, fewer extreme events occurred (Table S5), with sensitivity primarily to drought, resulting in cumulative production and economic losses of only 4.3% and 2.9% of the total losses, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig. S4).\u003c/p\u003e \u003cp\u003eFrom 1991 to 2019, production losses due to extreme events increased significantly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with a trend of 0.15\u0026nbsp;million tons yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). This was primarily driven by the yield rise for all grain crops and the expansion of maize\u0026rsquo;s harvested area, along with the increased frequency of heatwaves affecting maize and rice (Figs. S1 and Fig. S5a\u0026ndash;f). Moreover, the economic losses exhibited an even more significant increase (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) of 0.13\u0026nbsp;million 2019 USD yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), driven by rising agricultural producer prices (Fig. S5g\u0026ndash;i). In the last decade, the average annual production and economic losses were up to 10.5\u0026nbsp;million tons and USD 4.6\u0026nbsp;billion (Table S6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEvaluation of GGCMs\u003c/h3\u003e\n\u003cp\u003eGGCMs generally underestimated the yield losses (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Figs. S6\u0026thinsp;\u0026minus;\u0026thinsp;12), performing best for maize, consistent with previous evaluation study indicating that GGCMs best simulated the interannual maize yield variability\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Specifically, in GGCMI phase 1, four, ten, and seven out of the twelve models, and in GGCMI phase 3, four, eight, and eight out of the eleven models, captured the significant yield losses caused by heatwaves, drought, and extreme rainfall, respectively. However, nearly all models underestimated maize yield losses, highlighting a consistent bias in the model outputs. Wheat yield losses were similarly underestimated.\u003c/p\u003e \u003cp\u003eThe models performed worst in reproducing rice's response to extremes. In phase 1, no model could capture the rice yield loss, and most models even incorrectly predicted yield increases during extreme events, contradicting observed yield decreases. Phase 3 models, compared to their phase 1 counterparts, showed improved performance in capturing the effects of heatwaves and extreme rainfall on rice. Nevertheless, they continued to struggle with simulating the impact of extreme cold (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results not only confirm previous findings\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e that drought significantly reduced China's wheat yield and that maize yield declined due to water extremes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, d), but also reveal new insights through our comprehensive analysis of multiple extreme events and crop types. Specifically, we found that China's maize yield was also sensitive to heatwaves (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), similar to findings for the USA\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Rice yield was primarily influenced by heatwaves, extreme cold, and extreme rainfall (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). While a previous study indicated that extreme rainfall led to the greatest rice yield loss in China\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, we found that extreme cold resulted in the highest losses. This discrepancy likely stems from differences in defining extremes: we used meteorological thresholds (Methods), whereas the study\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e defined extremes based on known physiological impacts which are uncertain and therefore unsuitable for informing future impact projections.\u003c/p\u003e \u003cp\u003eThe impacts of weather and climate extremes on crop yields in China differ from global and European estimates based on the EM-DAT database. While our findings corroborated the global-scale study\u0026rsquo;s conclusion\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e that heatwaves and drought significantly reduced grain yield in China (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, c), we identified notable differences in the magnitude of these effects. Drought in China led to a larger yield loss of 7.3%, while heatwaves caused a smaller loss of 3.3%, in contrast to the global estimates of 5.1% and 7.6%\u003csup\u003e7\u003c/sup\u003e, respectively. Compared to analysis in Europe, the impacts of drought and heatwaves in China were less severe (9.0% and 7.3% in Europe, respectively), while extreme cold had a stronger effect in China than in Europe (1.9%vs 1.3%\u003csup\u003e8\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eMore importantly, our findings revealed that, compared to results at the global scale and in Europe and America, a greater variety of extreme events shows statistically significant impacts on more crop types in China. Globally, maize was the only cereal crop significantly affected by heatwaves and drought\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, whereas in China, wheat also showed high vulnerability to drought and rice to heatwaves. In addition, we identified significant yield losses due to extreme cold (1.9%) and extreme rainfall (3.0%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, d), impacts that were not observed as significant in the global-scale analysis\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Extreme rainfall significantly affected yields in China but had little impact in Europe\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Furthermore, rice exhibited little sensitivity to extreme events in America\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, while it was significantly affected by heatwaves, extreme cold, and extreme rainfall in China.\u003c/p\u003e \u003cp\u003eWe found that irrigation can buffer the influence of heatwaves, drought, and extreme rainfall on crop yields in China (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), aligning with findings in America\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Irrigation alleviates crop water stress and reduces canopy temperature through enhanced evaporative cooling, thus mitigating the adverse effects of heatwaves and drought\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Additionally, regions with well-developed irrigation infrastructure often adopt better land management practices, such as drainage systems and the use of flood-tolerant varieties\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, which may explain the smaller yield losses in irrigated croplands during extreme rainfall events (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eOur study provides comprehensive estimates of crop production and corresponding economic losses due to various weather and climate extremes in China. Our estimates show that these extremes resulted in an average annual production loss of 9.1\u0026nbsp;million tons, associated with an average economic loss of USD 3.1\u0026nbsp;billion (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The production loss contributed 13% of global grain production losses reported by FAO for all disasters\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, with Chinese heatwaves and drought alone causing annual declines of 3.0\u0026nbsp;million tons (11.1% of global losses) and 2.2\u0026nbsp;million tons (5.3% of global losses), respectively\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e (Table S4). The economic impact is equivalent to 2.5% of global economic losses in grain, cash, and energy crops as well as livestocks\u003csup\u003e15\u003c/sup\u003e and 11% of China's grain import value\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Even more concerning, the production and economic losses due to extremes in China have significantly increased over the past three decades. These numbers highlight China's critical role in global agricultural vulnerability and its growing implications for food security and international trade.\u003c/p\u003e \u003cp\u003eOur evaluation of 23 GGCMI crop models revealed their general underestimation of weather and climate extreme impacts for China, with some models even failing to capture yield reductions. This suggests that GGCM-based future yield projections may be overly optimistic, particularly concerning the projected rising frequency of heatwaves, drought, and extreme rainfall events\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGGCMI phase 3 models show improvements over their phase 1 predecessors in simulating the effects of heatwaves and extreme rainfall on rice yields. These improvements are likely attributed to developments in model and input data, such as more accurate input data of planting and harvest dates\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Nonetheless, significant challenges remain in the current modeling of extreme weather and climate stresses. Many GGCMs account for the impacts of heatwaves and extreme cold on growing season length (through affecting growing degree-day) and photosynthesis, and the impacts of drought on photosynthesis or leaf senescence (Table S7). However, most of them do not explicitly model the effects of heatwaves, drought, and extreme cold during the reproductive period when crop yields are most vulnerable (Supplementary Text 1). For example, heatwaves, drought, and extreme cold can reduce grain number through abortion and sterility, while drought can also shorten the grain-filling period, reducing grain size\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. When it comes to extreme rainfall, only a handful of models, specifically CGMS-WOFOST, the EPIC family, ACEA, and CYGMA, partially account for its effects, such as reducing photosynthesis, limiting leaf growth, inducing leaf senescence, or increasing nutrient leaching (Supplementary Text 1). Moreover, some critical mechanisms, such as extreme rainfall reduces effective panicles during the vegetative phase and decreases grain numbers during the reproductive phase for rice\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, are overlooked. In addition, even among models that do incorporate some of these critical influences, our evaluation suggests they still underestimate the effects. There is a clear need for substantial improvements in parameterization to accurately capture the full impacts of extreme events.\u003c/p\u003e \u003cp\u003eWhile our study offers crucial insights, it is essential to acknowledge several key limitations that suggest directions for future research. First, the sensitivity of crops to extremes varies with phenological stages\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. However, we did not differentiate the impacts of extreme events across these stages. Future research should provide a more comprehensive understanding of how extreme events affect crops through their lifecycle. Second, our study does not consider the varying intensities, spatial extent, and durations of extreme events on crop yields. Moreover, our study does not fully address the increasing prevalence of compound climate extremes. Recent research indicates that co-occurring extreme events can synergistically affect crop yields, potentially causing greater impacts than individual events\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. This emerging phenomenon highlights the urgent need for comprehensive studies that quantify the complex interactions and cumulative impacts of compound climate extremes on agricultural systems.\u003c/p\u003e \u003cp\u003eThis study provides a comprehensive quantification of weather and climate extreme impacts on grain yields in China, offering critical estimates of related production and economic losses. Our findings reveal the complex, region-specific nature of climate impacts on agriculture, emphasizing the need for tailored mitigation and adaptation strategies. We identify vulnerable crop types and key extreme events, laying the groundwork for targeted interventions such as crop-specific protection measures, optimized planting schedules, and climate-resilient crop breeding\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. These insights are crucial for guiding policy decisions, financial investments, and insurance frameworks to enhance agricultural resilience. Furthermore, our evaluation of GGCMs highlights the urgent need to improve the structural representation and parameterization of extreme event stresses, which is critical for refining future crop production projections and food security assessments.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eCrop data\u003c/h2\u003e \u003cp\u003eStatistical data on provincial-level grain yields and harvested areas for the three major grain crops (wheat, maize, and rice) from 1970 to 2019 were obtained from the National Bureau of Statistics of China (NBSC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.stats.gov.cn/english\u003c/span\u003e\u003cspan address=\"https://data.stats.gov.cn/english\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The linear trend of grain yields was removed, which was mainly related to the development of agricultural management in China\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe irrigated and rainfed harvested area distribution data were provided by GGCMI phase 3\u003csup\u003e20\u003c/sup\u003e, based on the MIRCA2000 (Monthly Irrigated and Rainfed Crop Areas around the year 2000)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and Siebert et al.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, and were held constant over time. The phenological data used to identify the extreme events that happened during the crop growing season, including the start of the vegetative growth period dates (green-up date for winter wheat, emergence date for spring wheat, transplanting date for rice, and V3 stage for maize) and maturity dates, were sourced from ChinaCropPhen1km dataset\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, which has a spatial resolution of 1 km and covers the period from 2000 to 2019. Note that the overwintering stage of winter wheat was not included in this study. The phenological data of each crop were interpolated and aggregated into provincial scales using the method of area-weighted averaging, and 2000 to 2019 averages were adopted.\u003c/p\u003e \u003cp\u003eTo estimate the economic losses, agricultural producer price data (USD/ton) for the three crops from 1991 to 2019 (the period for which agricultural producer price data is available) were sourced from the Food and Agriculture Organization of the United Nations (FAO)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Missing years were linearly interpolated between the adjacent values. The prices were converted to their 2019 equivalents using inflation data from the World Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.worldbank.org/\u003c/span\u003e\u003cspan address=\"https://data.worldbank.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to facilitate comparison.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eClimate data\u003c/h3\u003e\n\u003cp\u003eThe daily maximum temperature, minimum temperature, and precipitation from 1970 to 2019 were obtained from CN05.1\u003csup\u003e43\u003c/sup\u003e to identify heatwaves, extreme cold, and extreme rainfall events. The CN05.1 data was constructed based on interpolation from observation stations in China, with a spatial resolution of 0.25\u0026deg;. The monthly self-calibrating Palmer Drought Severity Index (scPDSI) from 1970 to 2019, with a spatial resolution of 0.5\u0026deg;, was used to identify drought\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. To identify weather and climate extremes affecting crops at the provincial level, all climate factors were aggregated into provincial scales using the harvested area distribution data for each crop.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCrop models\u003c/h2\u003e \u003cp\u003eTwelve GGCMs (CGMS-WOFOST, CLM4.5post-crop, EPIC-Boku, EPIC-IIASA, GEPIC, LPJ-GUESS, LPJmL, ORCHIDEE-crop, pAPSIM, pDSSAT, PEGASUS, and PEPIC) from GGCMI phase 1 alongside eleven GGCMs (ACEA, CROVER, CYGMA, EPIC-IIASA, ISAM, LandscapeDNDC, LPJmL, pDSSAT, PEPIC, PROMET, SIMPLACE-LINTUL5) from GGCMI phase 3 were evaluated in this study (Table S7). The GGCMI phase 1 project aimed to understand the skill of GGCM in reproducing observed historic crop yield\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, and GGCMI phase 3 also included historical simulations to evaluate GGCM performance\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Simulations of the twelve GGCMI phase 1 models were obtained from M\u0026uuml;ller et al.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Simulations of GGCMI phase 1 under default scenarios, which represent the highest simulation skill of each model, were used. The simulations were forced by the AgMERRA climate dataset for GGCMI phase 1 and by GSWP3-W5E5 (PROMET was forced by WFDE5) for GGCMI phase 3, with a spatial resolution of 0.5\u0026deg; (Table S7). Simulations for the period of 1980\u0026ndash;2010 (1981\u0026ndash;2009 for PROMET) were used.\u003c/p\u003e \u003cp\u003eTo ensure the alignment of the gridded GGCMI yield simulations for each growing season with the observed yields at the provincial scale for each calendar year, we first conducted temporal and spatial data preprocessing. Temporally, we aligned the yield of a growing season with the calendar year based on the harvest date calculated based on the actual planting date and days from planting to maturity provided by GGCMI\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Spatially, the simulations were then aggregated into provincial scales by weighting irrigated and rainfed simulated yields with their respective harvested areas. Finally, the provincial yields were detrended.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDefinitions of weather and climate extremes\u003c/h2\u003e \u003cp\u003eThis study quantified the impacts of four main weather and climate extremes (heatwaves, drought, extreme cold, and extreme rainfall) on crop yields, with definitions of each extreme event provided as follows. For weather extremes, a provincial heatwave event affecting a specific crop was defined as at least three consecutive days with daily maximum temperatures above 35\u0026deg;C in the crop\u0026rsquo;s planting regions during its growing season, following the definition of the Chinese Meteorological Administration heat warnings\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. A provincial extreme cold event affecting a specific crop was defined as at least six consecutive days with daily minimum temperatures below the 10th percentile of the minimum temperature distribution in the crop\u0026rsquo;s planting regions during its growing season, following the definition of the cold spell duration index (CSDI) recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI)\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. A provincial extreme rainfall event affecting a specific crop occurred when at least one day of daily precipitation exceeded 50 mm (38 mm or 25 mm) during the crop\u0026rsquo;s growing season in its planting regions where the mean annual precipitation was above 400 mm (between 200 mm\u0026ndash;400 mm or below 200 mm), following the definition provided by the China meteorological administration\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In terms of the climate extreme, according to previous studies\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, a provincial drought event affecting a specific crop was identified when the PDSI remained below \u0026minus;\u0026thinsp;3 for at least one month. For compound events, each type of extreme event was recorded separately.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEstimation of crop losses\u003c/h2\u003e \u003cp\u003eWe used the superposed epoch analysis (SEA) method to quantify the average impacts of weather and climate extremes on grain yields. SEA is a statistical method used to enhance the response signal of particular events while reducing background noise from extraneous factors, commonly applied to estimate the impacts of extreme events on crop yields\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. We defined years in which a specific type of weather and climate extreme affected a particular crop (even if the year co-occurring with other extreme events) as extreme event years for that crop due to the corresponding extreme event. For certain types of weather and climate extremes, we extracted a short time series of yield using a 7-year window, with the extreme event year positioned at the center. For an extreme event occurring in consecutive years, we averaged yield across those years to derive a single yield value affected by the extreme event. Yields before or after the center yield affected by extreme events were excluded from the time series. The time series were normalized relative to the average of the 3 years preceding and following the event to remove the absolute magnitude of provincial data from the signal. The 7-year time series was then centered on the year of extreme and averaged to generate a composited time series.\u003c/p\u003e \u003cp\u003eTo test the significance of impacts in the SEA, we generated a fictitious event set for each type of weather and climate extreme, with the same sample size as the true event set by randomly selecting year-province combinations from the entire set of years and provinces in which the true events occurred, excluding those included in the true event set. The fictitious event set was analyzed as the true to generate control composites time series. This testifying process was repeated 1,000 times for robustness. The yield was considered a significant deficit or surplus if the true composite in the year of extreme was less than 2.5% or greater than 97.5% of the 1000 control composites, corresponding to a two-tailed 95% confidence level. The deficit or surplus was then quantified by comparing the true composite with the mean of the control composites.\u003c/p\u003e \u003cp\u003eWe classified provinces into rainfed or irrigated dominated categories if the rainfed or irrigated crop area in the province accounted for more than half of the total crop area based on the harvested area distribution data. The influence of weather and climate extremes on grain yields was quantified separately for rainfed and irrigated dominated provinces to assess the differential influences of extreme events on these two types of croplands.\u003c/p\u003e \u003cp\u003eWe calculated the national interannual variations of crop yields as the mean coefficient of variation (CV) of detrended provincial yields during non-extreme years. We then compared this with the national average of yield losses.\u003c/p\u003e \u003cp\u003eBased on the quantified yield losses, for a given province, we estimated the provincial grain production loss (\u003cem\u003ePL\u003c/em\u003e) of each crop due to each weather and climate extreme in the year of extreme (\u003cem\u003ey\u003c/em\u003e) as:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{PL}_{y}={A}_{y}\\times\\:\\frac{{Y}_{y}}{1-YL}\\times\\:YL$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere A\u003csub\u003ey\u003c/sub\u003e and \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e is the statistical provincial crop harvested area and yield in the year of extreme (\u003cem\u003ey\u003c/em\u003e), \u003cem\u003eYL\u003c/em\u003e (%) is the quantified crop yield loss due to weather and climate extreme averaged over 1970\u0026thinsp;\u0026minus;\u0026thinsp;2019 based on the SEA. The use of \u003cem\u003eYL\u003c/em\u003e (%) is justified as the losses are overall stable over time, confirmed by Student's t-test comparing the relative reduction of sub-periods (1991\u0026thinsp;\u0026minus;\u0026thinsp;2004 and 2005\u0026thinsp;\u0026minus;\u0026thinsp;2019) against the relative reduction for 1970\u0026thinsp;\u0026minus;\u0026thinsp;2019 for all crop-extreme combinations, except for heatwave impacts on rice during 2005\u0026thinsp;\u0026minus;\u0026thinsp;2019 (Fig. S13). The estimated provincial grain production losses were then aggregated to the national total production loss. Subsequently, the national production losses were converted into economic losses based on the agricultural producer price.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe statistics on provincial-level grain yields and harvested areas are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.stats.gov.cn/english\u003c/span\u003e\u003cspan address=\"https://data.stats.gov.cn/english\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The crop phenological data are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6084/m9.figshare.8313530\u003c/span\u003e\u003cspan address=\"10.6084/m9.figshare.8313530\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The agricultural producer price data are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/faostat/en/#compare\u003c/span\u003e\u003cspan address=\"https://www.fao.org/faostat/en/#compare\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, and the inflation data are at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.worldbank.org/\u003c/span\u003e\u003cspan address=\"https://data.worldbank.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The climate data are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ccrc.iap.ac.cn/resource/detail?id=228\u003c/span\u003e\u003cspan address=\"https://ccrc.iap.ac.cn/resource/detail?id=228\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://crudata.uea.ac.uk/cru/data/drought/\u003c/span\u003e\u003cspan address=\"https://crudata.uea.ac.uk/cru/data/drought/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Yield simulations from GGCMI phase 1 crop models are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zenodo.org/\u003c/span\u003e\u003cspan address=\"https://zenodo.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (see Ref.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e), and phase 3 yield outputs and harvested area distribution data are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://agmip.org/aggrid-ggcmi/\u003c/span\u003e\u003cspan address=\"https://agmip.org/aggrid-ggcmi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCode availability\u003c/h2\u003e \u003cp\u003eAll of the python scripts used in the analyses are available from the corresponding author on request.\u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eF.L. conceived the research and designed the figures, D.Y. conducted the analyses and produced the figures and tables. J.J. and C.M. provided the GGCMI phases 1 and 3 simulations. D.Y. and F.L. wrote the manuscript with contributions from all authors.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eD.Y. and F.L. are supported by the Guangdong Major Project of Basic and Applied Basic Research (Grant No. 2021B0301030007), the National Key Research and Development Program of China (Grant Nos. 2017YFA0604302 and 2017YFA0604804), the National Natural Science Foundation of China (Grant No. 41875137), and the National Key Scientific and Technological Infrastructure project \u0026ldquo;Earth System Science Numerical Simulator Facility\u0026rdquo; (EarthLab). L.X. is supported by US National Science Foundation grant AGS-2017113. W.L. was supported by the National Natural Science Foundation of China (Grant No. 52239002 and 52109071). We also acknowledge Toshichika Iizumi, Zhaohui Lin, Zhongda Lin, Xiyan Xu, and Yaqiong Lu for their valuable advice and assistance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVogel, E. \u003cem\u003eet al.\u003c/em\u003e The effects of climate extremes on global agricultural yields. Environmental Research Letters 14, 054010 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIntergovernmental Panel On Climate Change (Ipcc). \u003cem\u003eClimate Change 2021 \u0026ndash; The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\u003c/em\u003e. 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Dynam. 11, 251\u0026ndash;266 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5629484/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5629484/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs the world's largest grain producer, China faces substantial challenges from weather and climate extremes, threatening domestic and global food security. Earlier studies focused narrowly on a single crop type, extreme event type, or small region. This study provides the first comprehensive analysis of heatwaves, drought, extreme cold, and extreme rainfall on wheat, maize, and rice yields in China from 1970 to 2019. We find these extremes reduce grain yields by 3.3, 7.3, 1.9, and 3.0%, respectively, amounting to 33.5, 66.6, 18.0, and 29.3% of the interannual yield variability. Chinese agriculture faces distinct vulnerabilities, with more diverse extremes significantly affecting more crop types compared to global, European, and American assessments. These yield reductions cause annual losses of 9.1\u0026nbsp;million tons in production and USD 3.1\u0026nbsp;billion, both increasing over time. Rainfed croplands suffer twice the yield losses of irrigated croplands, suggesting irrigation as an effective adaptation strategy. Current crop models largely underestimate these losses, indicating future climate impacts may be more severe than previously assessed.\u003c/p\u003e","manuscriptTitle":"Weather and Climate Extremes Drive Grain Yield Reductions and Economic Losses in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-29 11:15:34","doi":"10.21203/rs.3.rs-5629484/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dab9ef17-2923-4e9f-8789-1a6eec82672d","owner":[],"postedDate":"January 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43491191,"name":"Earth and environmental sciences/Natural hazards"},{"id":43491192,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate and Earth system modelling"}],"tags":[],"updatedAt":"2025-05-28T20:30:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-29 11:15:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5629484","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5629484","identity":"rs-5629484","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}