Flexible modeling of multivariate spatial extremes

Yan Gong, Raphaël Huser

Research output: Contribution to journalArticlepeer-review


We develop a novel multi-factor copula model for multivariate spatial extremes, which is designed to capture the different combinations of within- and cross-field extremal dependence structures between different spatial processes. Our proposed model, which can be seen as a multi-factor copula model, can capture all possible distinct combinations of extremal dependence structures within each individual spatial process while allowing flexible cross-field extremal dependence structures for both upper and lower tails. We show how to perform Bayesian inference for the proposed model using a Markov chain Monte Carlo algorithm based on carefully designed block proposals with an adaptive step size. In our real data application, we apply our model to study the upper and lower extremal dependence structures of the daily maximum air temperature (TMAX) and daily minimum air temperature (TMIN) from the state of Alabama in the southeastern United States. The fitted multivariate spatial model is found to provide a good fit in the lower and upper joint tails, both in terms of the spatial dependence structure within each individual process, as well as in terms of the cross-field dependence structure. Our results suggest that the TMAX and TMIN processes are quite strongly spatially dependent over the state of Alabama, and moderately cross-dependent. From a practical perspective, this implies that it may be worthwhile to model them jointly when interest lies in a computing spatial risk measures that involve both quantities.
Original languageEnglish (US)
Pages (from-to)100713
JournalSpatial Statistics
StatePublished - Nov 12 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-11-14
Acknowledged KAUST grant number(s): OSR-CRG2017-3434, OSR-CRG2020-4394
Acknowledgements: This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Awards No. OSR-CRG2017-3434 and No. OSR-CRG2020-4394.

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Statistics and Probability
  • Management, Monitoring, Policy and Law


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