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Due to climate change, we expect an exacerbation of fire in Europe and around the world, with major wildfire events extending to northern latitudes and boreal regions [1]. In this context, it is important to improve our capabilities to anticipate fire danger and understand its driving mechanisms at a global scale. As the earth is an interconnected system, large-scale processes can have an effect on the global climate and fire seasons. For example, extreme fires in Siberia have been linked to previous-year surface moisture conditions and anomalies in the Arctic Oscillation [2]. As part of the ESA-funded project SeasFire (https://seasfire.hua.gr), we gather and harmonize data related to seasonal fire drivers and develop deep learning models that are able to capture spatiotemporal associations with the goal to forecast burned area sizes on a seasonal scale, globally. We publish a global analysis-ready datacube for seasonal fire forecasting for the years 2001-2021 at a spatiotemporal resolution of 0.25 deg x 0.25 deg x 8 days [3]. The datacube includes a combination of variables describing the seasonal fire drivers , namely climate, vegetation, oceanic indices, human factors, land cover and the burned areas. We leverage the availability of big EO data and the advances in Deep Learning modeling [4, 5] to forecast global burned areas, capture the spatio-temporal interactions of the Earth System variables and identify potential teleconnections that determine wildfire regimes under the light of climate change. We present deep learning models that handle the Earth as a system, such as graph neural networks and transformer-based architectures. Applied on the prediction of wildfires at different temporal horizons we reveal that our deep learning models skillfully predict burned area patterns. Exploring the explanation of the models, we reveal important spatio-temporal links.Our approach, using AI to model the earth as a system and capture long spatio-temporal interactions, showcases the potential of an application-specific digital twin. The SeasFire datacube can be exploited as a baseline digital twin for modeling different natural hazards, including floods, heatwaves, and droughts. Thus, we will discuss insights and future directions for digital twins in anticipating climate extremes, inspired by our global wildfire prediction paradigm. [1] Wu, Chao, et al. "Historical and future global burned area with changing climate and human demography." One Earth 4.4 (2021): 517-530.[2] Kim, Jin-Soo, et al. "Extensive fires in southeastern Siberian permafrost linked to preceding Arctic Oscillation." Science advances 6.2 (2020): eaax3308.[3] Alonso, Lazaro, et al. Seasfire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth System. Zenodo, 15 July 2022, p., doi:10.5281/zenodo.6834584.[4] Kondylatos, Spyros et al. “Wildfire Danger Prediction and Understanding with Deep Learning.” Geophysical Research Letters, 2022. doi: 10.1029/2022GL099368[5] Prapas, Ioannis et al. “Deep Learning for Global Wildfire Forecasting.” NeurIPS 2022 workshop on Tackling Climate Change with Machine Learning, doi: 10.48550/arXiv.2211.00534

Climate change exacerbates the occurrence of extreme droughts and heatwaves, increasing the frequency and intensity of large wildfires across the globe. Forecasting wildfire danger and uncovering the drivers behind fire events become central for understanding relevant climate-land surface feedback and aiding wildfire management. In this work, we leverage Deep Learning (DL) to predict the next day's wildfire danger in a fire-prone part of the Eastern Mediterranean and explainable Artificial Intelligence (xAI) to diagnose model attributions. We implement DL models that capture the temporal and spatio-temporal context, generalize well for extreme wildfires, and demonstrate improved performance over the traditional Fire Weather Index. Leveraging xAI, we identify the substantial contribution of wetness-related variables and unveil the temporal focus of the models. The variability of the contribution of the input variables across wildfire events hints into different wildfire mechanisms. The presented methodology paves the way to more robust, accurate, and trustworthy data-driven anticipation of wildfires., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)