Few-shot learning for mid-latitude climate forecasts

Reliable prediction of climate variables and high-impact extremes in the midlatitudes is crucial for climate risk assessment, agricultural planning, water resource management, and disaster preparedness. However, conventional deep learning–based approaches for midlatitude climate prediction trained with dynamical climate models (e.g., CMIP models) can cause systematic errors in capturing the observed climate-relevant signals, ultimately limiting prediction skill. These limitations highlight the need to improve midlatitude prediction by detecting climate signals solely from the limited numbers of reliable observational climate data. To address the challenge of limited training samples, we employ the model-agnostic meta-learning (MAML) algorithm along with domain-knowledge-based data augmentation to predict mid-latitude winter temperatures. The proposed data augmentation is purely based on the observed data by defining the labels using large-scale climate variabilities associated with the target variable. The MAML-applied convolutional neural network (CNN) demonstrates superior correlation skills for winter temperature anomalies compared to a reference model (i.e., the CNN without MAML) and state-of-the-art dynamical forecast models across all target lead months during the boreal winter seasons. Moreover, occlusion sensitivity results reveal that the MAML model better captures the physical precursors that influence mid-latitude winter temperatures, resulting in more accurate predictions.