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Wildfires cause extensive damage to physical assets exposed to them. So far, assessing the risk of these events remains an understudied area of global disaster risk assessment. Probabilistic risk estimates covering the range and likelihood of devastating events are crucial for various applications such as prioritising adaptation measures and determining insurance pricing. Quantifying tail risks such as a one-in-a-hundred-year impact has important implications for disaster risk management, including the pricing of insurance. However, short observational time series render modelling efforts indispensable for risk assessments on a global scale.

In parallel, increasing data availability allows for the use of machine learning techniques to predict wildfire behaviour. In this context, an open-source wildfire risk model based on globally available data would facilitate the accessibility of such analysis to stakeholders from both …