This study presents the Surrogate Engine for Crop Simulations Framework (SECSF) 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, SECSF reproduces crop growth dynamics and harvest timing with high fidelity. Critically, SECSF extremely reduces computational costs enabling ensemble-scale inference suitable for operational pipelines. When driven by seasonal data, SECSF 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, SECSF 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框架（SECSF），该框架由一组深度学习模型构成，仅利用每日最高温度、最低温度及降水量来模拟基于过程的ECroPS模型。本研究以籽粒玉米和春大麦为模拟对象。经过ERA5强迫下的ECroPS模拟数据训练后，SECSF能以高保真度重现作物生长动态和收获时间。关键在于，SECSF极大降低了计算成本，使其适用于运行管线的集合尺度推理。当采用季节性数据驱动时，SECSF能捕捉欧洲范围内作物胁迫的年际变化和空间格局，并与独立监测数据保持一致，从而支持其作为概率性关注区域指标用于早期预警。在CMIP6 SSP3-7.0和SSP5-8.5情景下，SECSF一致地将地中海盆地识别为直至本世纪中期持续存在的产量风险热点区域，而中北欧地区则呈现混合信号。这些结果表明，一种精简且数据高效的替代模型能够在大陆尺度上提供稳健的季节性到气候性风险评估。