Thank you for inviting me to review for DDI. I enjoyed reading the manuscript entitled "Uncertainty in consensus predictions of plant species' vulnerability to climate change". The manuscript addresses an important global issue using a local use case and fits well within the scope of the journal. The manuscript has a clear storyline, the underlying materials and methods are well explained, which made it easy to follow.
My only comment concerns the choice of climate data. The spatial resolution of the study is about 270m, whereas GCMs are trained at much coarser resolution. There are some computational attempts to downscale the future climate data from GCMs to about 1000m. The authors used a bilinear interpolation to resample the data for about 4 times finer resolution, thus introducing a lot of uncertainty into the models. It would be great if the authors explicitly addressed this concern and discussed its potential implementation on the interpretation of the results. Furthermore, the author used two RCPs from two GCMs for 2040-2070 and for 2070-2100. Since IPCC AR6, the concept of RCPs has been extended to Shared Socioeconomic Pathways (SSPs). The authors could consider using bias-corrected CMIP6 climate data and move from RCPs to SSPs.
I would like to thank the authors and DDI for providing access to the R scripts behind the manuscript during the review process. Very helpful.