  We describe our submission to the Extreme Value Analysis 2019 Data Challenge
in which teams were asked to predict extremes of sea surface temperature
anomaly within spatio-temporal regions of missing data. We present a
computational framework which reconstructs missing data using convolutional
deep neural networks. Conditioned on incomplete data, we employ
autoencoder-like models as multivariate conditional distributions from which
possible reconstructions of the complete dataset are sampled using imputed
noise. In order to mitigate bias introduced by any one particular model, a
prediction ensemble is constructed to create the final distribution of extremal
values. Our method does not rely on expert knowledge in order to accurately
reproduce dynamic features of a complex oceanographic system with minimal
assumptions. The obtained results promise reusability and generalization to
other domains.
