  The global occurrence, scale, and frequency of wildfires pose significant
threats to ecosystem services and human livelihoods. To effectively quantify
and attribute the antecedent conditions for wildfires, a thorough understanding
of Earth system dynamics is imperative. In response, we introduce the SeasFire
datacube, a meticulously curated spatiotemporal dataset tailored for global
sub-seasonal to seasonal wildfire modeling via Earth observation. The SeasFire
datacube comprises of 59 variables encompassing climate, vegetation, oceanic
indices, and human factors, has an 8-day temporal resolution and a spatial
resolution of 0.25$^{\circ}$, and spans from 2001 to 2021. We showcase the
versatility of SeasFire for exploring the variability and seasonality of
wildfire drivers, modeling causal links between ocean-climate teleconnections
and wildfires, and predicting sub-seasonal wildfire patterns across multiple
timescales with a Deep Learning model. We publicly release the SeasFire
datacube and appeal to Earth system scientists and Machine Learning
practitioners to use it for an improved understanding and anticipation of
wildfires.
