Elena Xoplaki

Odysseas Vlachopoulos, Niklas Luther, Andrej Ceglar et al.

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.

J. Pérez-Aracil, C. Peláez-Rodríguez, Ronan McAdam et al.

Heatwaves (HWs) are extreme atmospheric events that produce significant societal and environmental impacts. Predicting these extreme events remains challenging, as their complex interactions with large-scale atmospheric and climatic variables are difficult to capture with traditional statistical and dynamical models. This work presents a general method for driver identification in extreme climate events. A novel framework (STCO-FS) is proposed to identify key immediate (short-term) HW drivers by combining clustering algorithms with an ensemble evolutionary algorithm. The framework analyzes spatio-temporal data, reduces dimensionality by grouping similar geographical nodes for each variable, and develops driver selection in spatial and temporal domains, identifying the best time lags between predictive variables and HW occurrences. The proposed method has been applied to analyze HWs in the Adda river basin in Italy. The approach effectively identifies significant variables influencing HWs in this region. This research can potentially enhance our understanding of HW drivers and predictability.