- 1State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster, Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Informatio
- 2Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, China
- 3Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
- 4Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, China
- 5Key Laboratory of Agricultural Environment, Ministry of Agriculture and Rural Affaire of PR China, Beijing, China
Northeastern China (NEC), known as the granary of China, is significantly affected by compound heat-drought events (CHDEs), which have detrimental impacts on maize production. This study aims to investigate the physical mechanisms underlying the occurrences of CHDEs on maize production in NEC. Our findings indicate that CHDEs are associated with anomalous positive geopotential height at 500 hPa, the presence of anticyclone at 850 hPa and a uniform downward motion in NEC, all of which are adverse to maize production. Using a year-to-year increment method, we reveal that several key factors collectively influence CHDEs and maize production in NEC, including sea ice concentration in the Barents Sea in May, sea surface temperature (SST) in the equatorial East Pacific in February and March, soil water over northwestern Siberia in April, and the North Atlantic Oscillation (NAO) in February. To differentiate the diverse influences of these key factors on CHDEs and maize production, we developed two distinct prediction models (Prediction Model #1 and #2). Both Prediction Model #1 (r=0.90, p<0.01) and #2 (r=0.91, p<0.01) demonstrate high correlation coefficients between predicted and observed values, as validated through leave-one-out cross-validation (Prediction Model #1: r=0.90, p<0.01; Prediction Model #2: r=0.90, p<0.01) and independent hindcasts (Prediction Model #1: r=0.72, p<0.01; Prediction Model #2: r=0.79, p<0.01). This study provides precise predictions of maize production in eastern China, offering significant safeguards for national food security.
How to cite: Zhou, Y., Li, H., Sun, B., Wang, H., Ju, H., Yuan, Y., and Zeng, J.: Predicting Maize Production in Northeastern China: Unraveling the Influence of Summer Compound Heat-Drought Events through Physical Mechanisms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7165, https://doi.org/10.5194/egusphere-egu26-7165, 2026.