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 degrees, 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.

全球野火的发生、规模和频率对生态系统服务和人类生计构成重大威胁。为了有效量化和归因于野火的先决条件，对地球系统动力学的彻底理解是必需的。为此，我们介绍了 SeasFire 数据立方体，这是一个经过精心策划的时空数据集，可用于通过地球观测进行全球亚季节到季节性野火建模。SeasFire 数据立方体包括 59 个变量，涵盖气候、植被、海洋指数和人类因素，在时间上具有 8 天的分辨率，在空间上具有 0.25 度的分辨率，时段为 2001 年至 2021 年。我们展示了 SeasFire 在探索野火驱动因素的变异性和季节性、建模海洋 - 气候相互关系与野火之间的因果关系以及使用深度学习模型预测多个时间尺度下的亚季节性野火模式的多功能性。我们公开发布了 SeasFire 数据立方体，并呼吁地球系统科学家和机器学习实践者使用该数据立方体来改善对野火的理解和预测。