Abstract Bushfire fuel hazard is determined by the type, amount, density and three‐dimensional distribution of plant biomass and litter. The fuel hazard represents a biological control on fire danger and may change in the future with plant growth patterns. Rising atmospheric CO2 concentration (Ca) stimulates plant productivity (‘fertilisation effect’) but also alters climate, leading to a ‘climatic effect’. Both effects have impacts on future vegetation and thus fuel hazard. Quantifying these effects is an important component of predicting future fire regimes and evaluating fire management options. Here, we combined a machine learning algorithm that incorporates the power of large fine spatial resolution (i.e. 90 m) datasets with a novel optimality model that accounts for the climatic and fertilisation effects on vegetation cover. We demonstrated the usefulness and practicality of this framework by predicting fuel hazard across the state of Victoria in Australia. We fitted and evaluated the models with long‐term (i.e. 20 years), ground‐based fuel observations. The models achieved strong agreement with observations across the fuel hazard range (accuracy >65%). We found fuel hazard increased more in dry environments due to future climate and Ca. The contribution of the ‘fertilisation effect’ to future fuel hazard varied spatially by up to 12%. The predictions of future fuel hazard are directly useful to inform fire mitigation policies and as a reference for climate model projections to account for fire impacts. Synthesis and applications: Climate change and rising Ca have profound impacts on vegetation and thus fuel load. Operational fire management and future fire risk forecasts will benefit from our realistic fuel load prediction framework that incorporates plant responses and fine soil and terrain attributes.

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