Understanding the relationships between large-scale, low-frequency climate variability modes, fire weather conditions and lighting-ignited wildfires has implications for fire-weather prediction, fire management and conservation. This article proposes a Bayesian network framework for quantifying the influence of climate modes on fire weather conditions and occurrence of lightning-ignited wildfires. The main objectives are to describe and demonstrate a probabilistic framework for identifying and quantifying the joint and individual relationships that comprise the climate-wildfire system; gain insight into potential causal mechanisms and pathways; gauge the influence of climate modes on fire weather and lightning-ignition relative to that of local-scale conditions alone; assess the predictive skill of the network; and motivate the use of techniques that are intuitive, flexible and for which user‐friendly software is freely available. A case study illustrates the application of the framework to a forested region in southwest Australia. Indices for six climate variability modes are considered along with two hazard variables (observed fire weather conditions and prescribed burn area), and a 41-year record of lightning-ignited wildfire counts. Using the case study data set, we demonstrate that the proposed framework: (1) is based on reasonable assumptions provided the joint density of the variables is converted to multivariate normal; (2) generates a parsimonious and interpretable network architecture; (3) identifies known or partially known relationships between the variables; (4) has potential to be used in a predictive setting for fire weather conditions; and (5) climate modes are more directly related to fire weather conditions than to lightning-ignition counts.

了解大规模，低频气候变异模式，火灾天气情况和照明点燃的野火之间的关系，对火灾天气的预测，火灾管理和保护具有重要意义。本文提出了一种贝叶斯网络框架，用于量化气候模式对火灾天气条件和闪电点燃的野火的影响。主要目标是描述和演示一个概率框架，用于识别和量化构成气候-野火系统的联合和个人关系；了解潜在的因果机制和途径；衡量气候模式对火灾天气和闪电点火的影响，而不是仅仅针对当地条件的影响；评估网络的预测能力；并鼓励使用直观，灵活且免费提供用户友好软件的技术。案例研究说明了该框架在澳大利亚西南部森林地区的应用。考虑了六个气候变异模式的指标以及两个危害变量（观察到的火灾天气条件和规定的燃烧面积），以及闪电点燃的野火计数的41年记录。使用案例研究数据集，我们证明了所提出的框架：（1）基于合理的假设，前提是将变量的联合密度转换为多元正态；（2）生成简约且可解释的网络体系结构；（3）识别变量之间的已知或部分已知的关系；（4）有可能用于火灾天气情况的预测环境中；