Predicting rare extreme events such as wildfires from meteorological data requires models that remain reliable under evolving environmental conditions. This problem is inherently long-tailed: wildfire events are rare but high-impact, while most observations correspond to non-fire conditions, causing standard learning objectives to underemphasize the minority class (fire) that matters most. In addition, models trained on historical distributions often fail under distribution shifts, exhibiting degraded performance in new environments. To this end, we propose Environment-Adaptive Preference Optimization (EAPO), a framework that adapts prediction to the target environment with long-tail distribution. Given a new input distribution, we first construct distribution-aligned datasets via $k$-nearest neighbor retrieval. We then perform a hybrid fine-tuning procedure on this local manifold, combining supervised learning with preference optimization, as well as emphasizing on rare extreme events. EAPO refines decision boundaries while avoiding conflicting signals from heterogeneous training data. We evaluate EAPO on a real-world wildfire prediction task with environmental shifts. EAPO achieves robust performance (ROC-AUC 0.7310) and improves detection in extreme regimes, demonstrating its effectiveness in dynamic wildfire prediction systems.

翻译：从气象数据预测野火等罕见极端事件，需要模型在环境条件动态变化下保持可靠性。这一问题本质上是长尾分布问题：野火事件虽属罕见却影响巨大，而多数观测数据对应非火情状况，导致标准学习目标会弱化最关键的少数类（火灾）的关注。此外，基于历史分布训练的模型在面对分布偏移时往往失效，在新环境中表现退化。为此，我们提出环境自适应偏好优化（EAPO）框架，该框架能适配具有长尾分布的目标环境进行预测。针对新输入分布，我们首先通过k近邻检索构建分布对齐的数据集，然后在该局部流形上执行混合微调流程，将监督学习与偏好优化相结合，并重点强化对罕见极端事件的关注。EAPO在优化决策边界的同时，避免了来自异构训练数据的信号冲突。我们在存在环境偏移的真实野火预测任务上评估了EAPO。该框架实现了稳健性能（ROC-AUC 0.7310），并提升了极端场景下的检测能力，验证了其在动态野火预测系统中的有效性。