Wildfires, as an integral component of the Earth system, are governed by a complex interplay of atmospheric, oceanic, and terrestrial processes spanning a vast range of spatiotemporal scales. Modeling their global activity on large timescales is therefore a critical yet challenging task. While deep learning has recently achieved significant breakthroughs in global weather forecasting, its potential for global wildfire behavior prediction remains underexplored. In this work, we reframe this problem and introduce the Hierarchical Graph ODE (HiGO), a novel framework designed to learn the multi-scale, continuous-time dynamics of wildfires. Specifically, we represent the Earth system as a multi-level graph hierarchy and propose an adaptive filtering message passing mechanism for both intra- and inter-level information flow, enabling more effective feature extraction and fusion. Furthermore, we incorporate GNN-parameterized Neural ODE modules at multiple levels to explicitly learn the continuous dynamics inherent to each scale. Through extensive experiments on the SeasFire Cube dataset, we demonstrate that HiGO significantly outperforms state-of-the-art baselines on long-range wildfire forecasting. Moreover, its continuous-time predictions exhibit strong observational consistency, highlighting its potential for real-world applications.

翻译：野火作为地球系统的重要组成部分，其活动受到跨越广阔时空尺度的大气、海洋和陆地过程之间复杂相互作用的调控。因此，在大时间尺度上建模其全球活动是一项关键且具有挑战性的任务。尽管深度学习近期在全球天气预报领域取得了重大突破，但其在全球野火行为预测方面的潜力仍未得到充分探索。在本工作中，我们重新审视了这一问题，并引入了层次图常微分方程（HiGO）这一新颖框架，旨在学习野火的多尺度、连续时间动力学。具体而言，我们将地球系统表示为一个多层级的图结构，并提出了一种用于层级内和层级间信息流的自适应滤波消息传递机制，从而实现更有效的特征提取与融合。此外，我们在多个层级中集成了由GNN参数化的神经ODE模块，以显式地学习每个尺度固有的连续动力学。通过在SeasFire Cube数据集上进行的大量实验，我们证明HiGO在长期野火预测任务上显著优于现有最先进的基线方法。此外，其连续时间预测结果展现出与观测数据的高度一致性，凸显了其在现实世界应用中的潜力。