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The global occurrence, scale, and frequency of wildfires pose significantthreats to ecosystem services and human livelihoods. To effectively quantifyand attribute the antecedent conditions for wildfires, a thorough understandingof Earth system dynamics is imperative. In response, we introduce the SeasFiredatacube, a meticulously curated spatiotemporal dataset tailored for globalsub-seasonal to seasonal wildfire modeling via Earth observation. The SeasFiredatacube comprises of 59 variables encompassing climate, vegetation, oceanicindices, and human factors, has an 8-day temporal resolution and a spatialresolution of 0.25$^{\circ}$, and spans from 2001 to 2021. We showcase theversatility of SeasFire for exploring the variability and seasonality ofwildfire drivers, modeling causal links between ocean-climate teleconnectionsand wildfires, and predicting sub-seasonal wildfire patterns across multipletimescales with a Deep Learning model. We publicly release the SeasFiredatacube and appeal to Earth system scientists and Machine Learningpractitioners to use it for an improved understanding and anticipation ofwildfires.

SeasFire as a Multivariate Earth System Datacube for Wildfire Dynamics