To address the challenge of limited training samples, this study employs the model-agnostic meta-learning (MAML) algorithm along with domain-knowledge-based data augmentation to predict winter temperatures on the Korean Peninsula. While data augmentation has been achieved by using global climate model simulations, the proposed augmentation is purely based on the observed data by defining the labels using large-scale climate variabilities associated with the Korean winter temperatures. The MAML-applied convolutional neural network (CNN) (referred to as the MAML model) demonstrates superior correlation skills for Korean 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. Sensitivity experiments show that the domain-knowledge-based data augmentation enhances the forecast skill of the MAML model. Moreover, occlusion sensitivity results reveal that the MAML model better captures the physical precursors that influence Korean winter temperatures, resulting in more accurate predictions.

为了解决训练样本有限的挑战，本研究采用与模型无关的元学习 （MAML） 算法以及基于领域知识的数据增强来预测朝鲜半岛的冬季温度。虽然通过使用全球气候模式模拟实现了数据增强，但拟议的增强完全基于观测数据，方法是使用与韩国冬季温度相关的大尺度气候变率来定义标签。与参考模型（即没有 MAML 的 CNN）和最先进的动态预报模型相比，MAML 应用的卷积神经网络 （CNN）（简称 MAML 模型）在北方冬季的所有目标提前月份表现出卓越的韩国温度异常相关技能。敏感性实验表明，基于领域知识的数据增强增强了 MAML 模型的预测技能。此外，遮挡敏感性结果表明，MAML 模型更好地捕获了影响韩国冬季温度的物理前体，从而获得更准确的预测。