  Wildfires are increasingly exacerbated as a result of climate change,
necessitating advanced proactive measures for effective mitigation. It is
important to forecast wildfires weeks and months in advance to plan forest fuel
management, resource procurement and allocation. To achieve such accurate
long-term forecasts at a global scale, it is crucial to employ models that
account for the Earth system's inherent spatio-temporal interactions, such as
memory effects and teleconnections. We propose a teleconnection-driven vision
transformer (TeleViT), capable of treating the Earth as one interconnected
system, integrating fine-grained local-scale inputs with global-scale inputs,
such as climate indices and coarse-grained global variables. Through
comprehensive experimentation, we demonstrate the superiority of TeleViT in
accurately predicting global burned area patterns for various forecasting
windows, up to four months in advance. The gain is especially pronounced in
larger forecasting windows, demonstrating the improved ability of deep learning
models that exploit teleconnections to capture Earth system dynamics. Code
available at https://github.com/Orion-Ai-Lab/TeleViT.
