  Large, non-stationary spatio-temporal data are ubiquitous in modern
statistical applications, and the modeling of spatio-temporal extremes is
crucial for assessing risks in environmental sciences among others. While the
modeling of extremes is challenging in itself, the prediction of rare events at
unobserved spatial locations and time points is even more difficult. In this
editorial, we describe the data competition that was organized for the 11th
international conference on Extreme-Value Analysis (EVA 2019), for which
several teams modeled and predicted Red Sea surface temperature extremes over
space and time. After introducing the dataset and the goal of the competition,
we disclose the final ranking of the teams, and we finally discuss some
interesting outcomes and future challenges.
