Résumé
Fire is a key Earth system process, driving variability in the global carbon cycle through CO2 emissions into the atmosphere and subsequent CO2 uptake through vegetation recovery after fires. Global spatiotemporally consistent datasets on burned area have been available since the beginning of the satellite era in the 1980s, but they are sparse prior to that date. In this study, we reconstructed global monthly burned area at a resolution of 0.5° × 0.5° from 1901 to 2020 using machine learning models trained on satellite-based observations of burned area between 2003 and 2020, with the goal of reconstructing long-term burned area information to constrain historical fire simulations. We first conducted a classification model to separate grid cells with extreme (burned area ≥ the 90th percentile in a given region) or regular fires. We then trained separate regression models for grid cells with extreme or regular fires. Both the classification and regression models were trained on a satellite-based burned area product (FireCCI51), using explanatory variables related to climate, vegetation and human activities. The trained models can well reproduce the long-term spatial patterns (slopes = 0.70–1.28 and R2 = 0.69–0.98 spatially), inter-annual variability and seasonality of the satellite-based burned area observations. After applying the trained model to the historical period, the predicted annual global total burned area ranges from 3.46×106 to 4.58×106 km2 yr−1 over 1901–2020 with regular and extreme fires accounting for 1.36×106–1.74×106 and 2.00×106–3.03×106 km2 yr−1, respectively. Our models estimate a global decrease in burned area during 1901–1978 (slope = -0.009×106 km2 yr−2), followed by an increase during 1978–2008 (slope = 0.020×106 km2 yr−2), and then a stronger decline in 2008–2020 (slope = -0.049×106 km2 yr−2). Africa was the continent with the largest burned area globally during 1901–2020, and its trends also dominated the global trends. We validated our predictions against charcoal records, and our product exhibits a high overall accuracy in simulating fire occurrence (>80 %) in boreal North America, southern Europe, South America, Africa and southeast Australia, but the overall accuracy is relatively lower in northern Europe and Asia (<50 %). In addition, we compared our burned area data with multiple independent regional burned area maps in Canada, the USA, Brazil, Chile and Europe, and found general consistency in the spatial patterns (linear regression slopes ranging 0.84–1.38 spatially) and the inter-annual variability. The global monthly 0.5° × 0.5° burned area fraction maps for 1901–2020 presented by this study can be downloaded for free from https://doi.org/10.5281/zenodo.14191467 (Guo and Li, 2024).