https://orcid.org/0000-0002-9439-6872
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Anthony C Davison, Raphaël de Fondeville, Anthony C. Davison and Raphaël de Fondeville’s contribution to the Discussion of ‘Inference for extreme spatial temperature events in a changing climate with application to Ireland’ by Healy et al., Journal of the Royal Statistical Society Series C: Applied Statistics, Volume 74, Issue 2, March 2025, Page 314, https://doi.org/10.1093/jrsssc/qlae081
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We congratulate the authors on their paper, which puts some of the ideas in de Fondeville and Davison (2018) to good use and suggests innovative approaches to dealing with issues not previously discussed in the r-Pareto context, such as the effect of missing data and the extrapolation of station data to unmonitored locations.
Major environmental events due to ‘heat domes’ seem to have become more common. The corresponding heatwaves are spatially large and can smash previous temperature records, as happened in 2021 in North America, when records were set across western Canada. Naive fits of extreme-value models to temperature maxima invariably result in a negative estimated shape parameter, as shown in Table 2 of the paper, which implies that the range of future values has an upper bound. Such a bound based on analysis of the previous data suggested that the 2021 event would be impossible, but of course it happened. One way to deal with this would be to construct a mixture distribution, perhaps with event probabilities dependent on climatic and/or meteorological variables. Major heatwaves are often associated with blocking anticyclones: would it be feasible to use such events to construct a more complex, but perhaps more realistic, model, rather than treating heatwaves as identically distributed? Otherwise we might insert the knowledge that such events might arise, for example using a penalized likelihood when estimating the shape parameter: do the authors think this might be helpful?
