A global database of extreme fire events from satellite data from 2003 to 2022

Abstract. Extreme fires represent a significant threat due to their impacts on climate, ecosystems, and society. Despite their increasing prevalence, their definition remains controversial, as their characteristics vary depending on the region considered. In this article, we present the first version of the Extreme Fire Events (EFEs) database, a global dataset of extreme fires in NetCDF format containing monthly rasters on a regular grid with a spatial resolution of 0.25 degrees. The database includes the period 2003–2022, when a consistent satellite record was available. The basic unit of analysis is a cell-month event (CME), which represents aggregated fire activity within a grid cell during a given month. The identification of extreme events was based on two main satellite-derived variables: Burned Area (BA) from the European Space Agency’s FireCCI51 dataset and Fire Radiative Power (FRP) obtained from the NASA MCD14ML active fire product. Both variables were derived from the MODIS sensor. They were aggregated to the spatial and temporal scale defined for the CMEs and were used to compute standardised anomalies within each of the 55 defined regions, in order to account for spatial and seasonal differences in fire activity in the main global biomes. A CME was classified as an EFE when it presented anomalous values in both variables according to the established regional thresholds. Further, for each EFE, the database also indicates if any fire perimeter from the FRY v2.0 dataset identified as extreme by a certain attribute (fire size, duration, mean FRP, rate of spread and severity) overlapped with the CME. The database includes 19,951 EFEs between 2003 and 2022, with the highest frequency in 2010 and 2007, and the lowest in 2013. The dataset is intended for climate and Earth System modellers aiming to understand the causes and impacts of EFEs, as well as to forecast their occurrence under future scenarios or include them in broader Earth System models.