In recent decades, the intensification of wildfire activity in western Canada has resulted in substantial socio-economic and environmental losses. Accurate wildfire risk prediction is hindered by the intrinsic stochasticity of ignition and spread and by nonlinear interactions among fuel conditions, meteorology, climate variability, topography, and human activities, challenging the reliability and interpretability of purely data-driven models. We propose a trustworthy data-driven wildfire risk prediction framework based on long-sequence, multi-scale temporal modeling, which integrates heterogeneous drivers while explicitly quantifying predictive uncertainty and enabling process-level interpretation. Evaluated over western Canada during the record-breaking 2023 and 2024 fire seasons, the proposed model outperforms existing time-series approaches, achieving an F1 score of 0.90 and a PR-AUC of 0.98 with low computational cost. Uncertainty-aware analysis reveals structured spatial and seasonal patterns in predictive confidence, highlighting increased uncertainty associated with ambiguous predictions and spatiotemporal decision boundaries. SHAP-based interpretation provides mechanistic understanding of wildfire controls, showing that temperature-related drivers dominate wildfire risk in both years, while moisture-related constraints play a stronger role in shaping spatial and land-cover-specific contrasts in 2024 compared to the widespread hot and dry conditions of 2023. Data and code are available at https://github.com/SynUW/mmFire.

翻译：近几十年来，加拿大西部日益加剧的野火活动已造成重大的社会经济与环境损失。由于起火与蔓延过程固有的随机性，以及可燃物状况、气象条件、气候变率、地形和人类活动之间的非线性相互作用，精确的野火风险预测面临挑战，这也影响了纯数据驱动模型的可靠性与可解释性。我们提出一个基于长序列、多尺度时序建模的可信数据驱动野火风险预测框架，该框架在整合异质性驱动因子的同时，显式量化预测不确定性并支持过程层面的解释。在破纪录的2023年和2024年火灾季节期间，于加拿大西部进行的评估表明，所提模型优于现有时间序列方法，以较低计算成本实现了0.90的F1分数和0.98的PR-AUC。基于不确定性的分析揭示了预测置信度在空间和季节上的结构化模式，突显了与模糊预测及时空决策边界相关的不确定性增加。基于SHAP的解释提供了对野火控制机制的深入理解，表明温度相关驱动因子在两年中均主导野火风险，而与湿度相关的约束条件在2024年对塑造空间及特定土地覆盖类型对比的作用比2023年更为显著——相较于2023年普遍炎热干燥的条件。数据与代码可在 https://github.com/SynUW/mmFire 获取。