  Wildfire forecasting is of paramount importance for disaster risk reduction
and environmental sustainability. We approach daily fire danger prediction as a
machine learning task, using historical Earth observation data from the last
decade to predict next-day's fire danger. To that end, we collect, pre-process
and harmonize an open-access datacube, featuring a set of covariates that
jointly affect the fire occurrence and spread, such as weather conditions,
satellite-derived products, topography features and variables related to human
activity. We implement a variety of Deep Learning (DL) models to capture the
spatial, temporal or spatio-temporal context and compare them against a Random
Forest (RF) baseline. We find that either spatial or temporal context is enough
to surpass the RF, while a ConvLSTM that exploits the spatio-temporal context
performs best with a test Area Under the Receiver Operating Characteristic of
0.926. Our DL-based proof-of-concept provides national-scale daily fire danger
maps at a much higher spatial resolution than existing operational solutions.
