text: 23,156,1479069371037364225,1215010368759701505,Eleanor D'Arcy,"First paper day🎉 <LINK>. We propose a methodology for predicting the number and sizes of wildfires in the US, with an emphasis on extreme events🚒🔥 This is joint work with @callumbarltrop, Rob Shooter & Emma Simpson, and motivated by the #EVA2021 data challenge",https://arxiv.org/abs/2112.15372,"This paper details the methodology proposed by the \textit{Lancaster Ducks} team for the EVA 2021 conference data challenge. This aim of this challenge was to predict the number and size of wildfires over the contiguous US between 1993-2015, with more importance placed on extreme events. Our approach proposes separate methods for modelling the bodies and tails of the distributions of both wildfire variables. For the former, a hierarchical clustering technique is proposed to first group similar locations, with a non-parametric approach subsequently used to model the non-extreme data. To capture tail behaviour, separate techniques derived from univariate extreme value theory are proposed for both variables. For the count data, a generalised Pareto distribution with a generalised additive model structure is used to capture effects from covariates on values above a high threshold. For burnt area, a non-stationary generalised Pareto distribution enables us to capture the tail behaviour of proportions obtained through a transformation of observed area data. The resulting predictions are shown to perform reasonably well, improving on the benchmark method proposed in the challenge outline. We also provide a discussion about the limitations of our modelling framework and evaluate ways in which it could be extended. ","A flexible, semi-parametric, cluster-based approach for predicting