The increasing frequency and duration of severe fire events in Australia further necessitate accurate and timely forecasting to mitigate their consequences. This study evaluated the performance of two distinct approaches to forecasting extreme fire danger at two- to three-week lead times for the period 2003 to 2017: the official Australian climate simulation dynamical model and a statistical model based on climate drivers. We employed linear logistic regression to develop the statistical model, assessing the influence of individual climate drivers using single linear regression. The performance of both models was evaluated through case studies of three significant extreme fire events in Australia: the Canberra (2003), Black Saturday (2009), and Pinery (2015) fires. The results revealed that ACCESS-S2 generally underestimated the spatial extent of all three extreme FBI events, but with accuracy scores ranging from 0.66 to 0.86 across the case studies. Conversely, the statistical model tended to overpredict the area affected by extreme FBI, with high false alarm ratios between 0.44 and 0.66. However, the statistical model demonstrated higher probability of detection scores, ranging from 0.57 to 0.87 compared with 0.03 to 0.57 for the dynamic model. These findings highlight the complementary strengths and limitations of both forecasting approaches. Integrating dynamical and statistical models with transparent communication of their uncertainties could potentially improve accuracy and reduce false alarms. This can be achieved through hybrid forecasting, combined with visual inspection and comparison between the statistical and dynamical forecasts. Hybrid forecasting also has the potential to increase forecast lead times to up to several months, ultimately aiding in decision-making and resource allocation for fire management.

中文翻译：

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澳大利亚火灾行为指数极端情况的统计预测模型

澳大利亚严重火灾事件的频率和持续时间不断增加，进一步需要准确和及时的预测，以减轻其后果。这项研究评估了 2003 年至 2017 年期间在两到三周的时间内预测极端火灾危险的两种不同方法的性能：澳大利亚官方气候模拟动态模型和基于气候驱动因素的统计模型。我们采用线性逻辑回归来开发统计模型，使用单一线性回归评估各个气候驱动因素的影响。通过对澳大利亚三起重大极端火灾事件的案例研究评估了这两种模型的性能：堪培拉火灾（2003 年）、黑色星期六火灾（2009 年）和松树火灾（2015 年）。结果显示，ACCESS-S2 通常低估了所有三个 FBI 极端事件的空间范围，但在整个案例研究中，准确度得分在 0.66 到 0.86 之间。相反，统计模型往往会高估受极端 FBI 影响的区域，误报率很高，在 0.44 到 0.66 之间。然而，统计模型表现出更高的检测分数概率，范围为 0.57 至 0.87，而动态模型为 0.03 至 0.57。这些发现凸显了两种预测方法的互补优势和局限性。将动态和统计模型与其不确定性的透明沟通相结合，可能会提高准确性并减少误报。这可以通过混合预测，结合目视检查以及统计预测和动态预测之间的比较来实现。混合预报还有可能将预报提前时间延长至长达几个月，最终有助于火灾管理的决策和资源分配。