  Deep learning-based models have recently outperformed state-of-the-art
seasonal forecasting models, such as for predicting El Ni\~no-Southern
Oscillation (ENSO). However, current deep learning models are based on
convolutional neural networks which are difficult to interpret and can fail to
model large-scale atmospheric patterns. In comparison, graph neural networks
(GNNs) are capable of modeling large-scale spatial dependencies and are more
interpretable due to the explicit modeling of information flow through edge
connections. We propose the first application of graph neural networks to
seasonal forecasting. We design a novel graph connectivity learning module that
enables our GNN model to learn large-scale spatial interactions jointly with
the actual ENSO forecasting task. Our model, \graphino, outperforms
state-of-the-art deep learning-based models for forecasts up to six months
ahead. Additionally, we show that our model is more interpretable as it learns
sensible connectivity structures that correlate with the ENSO anomaly pattern.
