The ‘super-station’ approach has been adopted since 1980s as a pragmatic method of improving extreme‑value predictions by grouping short-length datasets from several measurement stations to become a larger dataset to reduce uncertainties due to random sampling variation.  El Rafei et al. (2023, https://doi.org/10.1029/2023GL105286) analyzed reanalysis, and randomly generated, wind extremes datasets and suggested that this technique can introduce unexpected biases in typical situations.  We complement their work and demonstrate by Monte-Carlo simulation, assuming the same number of grouped stations and data lengths used, that applying the grouping technique to samples of properly de-trended datasets to meet the homogeneity assumption does not lead to biased prediction of extremes.  In addition, the grouping technique effectively reduces the uncertainty and sampling errors that result from short-length datasets from individual stations of consistent meteorology .