This study presents the Surrogate Engine for Crop Simulations (SECS) a group of deep-learning models that emulate the process-based ECroPS model using only daily maximum and minimum temperature and precipitation. In this study we emulate grain maize and spring barley. Trained on ERA5-forced ECroPS simulations, SECS reproduces crop growth dynamics and harvest timing with high fidelity. Critically, SECS extremely reduces computational costs enabling ensemble-scale inference suitable for operational pipelines. When driven by seasonal data, SECS captures the interannual and spatial patterns of crop stress across Europe and aligns with independent monitoring, supporting its use as a probabilistic Areas of Concern indicator for early warning. Under CMIP6 SSP3-7.0 and SSP5-8.5 scenarios, SECS consistently identifies the Mediterranean basin as a persistent hotspot of yield risk through mid-century, with central-northern Europe showing mixed signals. These results demonstrate that a streamlined, data-efficient emulator can provide robust seasonal-to-climate risk assessments at continental scale.

翻译：本研究提出了作物模拟替代引擎（SECS），这是一组仅利用日最高/最低气温和降水数据来模拟基于过程的ECroPS模型的深度学习模型。本研究以谷物玉米和春大麦为模拟对象。基于ERA5驱动的ECroPS模拟数据训练后，SECS能够高保真地复现作物生长动态和收获时间。关键的是，SECS极大降低了计算成本，可实现适用于业务化流程的集合规模推断。当采用季节数据驱动时，SECS能够捕捉全欧洲作物胁迫的年际和空间格局，并与独立监测结果吻合，支持其作为早期预警的概率性“关注区域”指标。在CMIP6 SSP3-7.0和SSP5-8.5情景下，SECS持续识别出地中海流域直至本世纪中叶均为产量风险的持续热点区域，而中北欧地区则呈现混合信号。这些结果表明，简化的数据高效模拟器能够在大陆尺度提供稳健的季节性至气候风险评估。