This paper describes a new open access dataset that will be of great value to hydrologists and others in the earth sciences. It is clear and well written and includes excellent background on NZ's climate, landscape and geology. I recommend that the paper is published if the following minor comments are addressed satisfactorily.

Lines 140-145: I would recommend using a more recent source for future estimates of temperature and rainfall changes instead of King 2010 as this is based on NIWA's modelling done nearly 20 years ago for AR4. For instance:

Peter Gibson, et al., 2025, Downscaled CMIP6 future climate projections for New Zealand: climatology and extremes, Weather and Climate Extremes, https://doi.org/10.1016/j.wace.2025.100784. 

Also when discussing changes in climate, the reference period needs to be included; e.g. "expected to warm by 1 degC by 2040 relative to the 1986-2005 average". In addition, as currently written only a single scenario has been explored (probably King 2010's mid-range scenario), but this choice needs to be highlighted and the scenario information included for context.

In the context of this dataset, it might also be of interest to describe how NZ's climate has changed over the past 60 years (for example referencing the 'seven station series' (https://niwa.co.nz/climate-and-weather/nz-temperature-record/seven-station-series-temperature-data) or the following recent research on changing climate normals, although there are many other studies that could be used.

Srinivasan, R., et al. (2024). Moving to a new normal: Analysis of shifting climate normals in New Zealand. International Journal of Climatology, 44(10), 3240–3263. https://doi.org/10.1002/joc.8521

Figure 4 and related text: Fig 4a units should be in %. Coefficient of variation is a normalised ratio of SD to MEAN, so either expressed as a fraction or %. The incorrect units are also in Table 3.

Line 253: Related to above, the sentence "Rainfall variability is highest in the eastern parts of both of the North and South Island" is not correct. Annual rainfall variability relative to the annual mean is highest in east (i.e. coefficient of variation).  Variability (e.g. variance or standard deviation) of annual rainfall is greatest in the higher elevation parts of the West Coast. Without showing a plot of annual mean rainfall, a reader unfamiliar with NZ, might come away from this part of the paper not realising that the highest rainfalls are on the west. I recommend that Fig 4 also include a map of mean annual rainfall.

I agree with the other comments posted about this manuscript - this new open-access dataset will be a great new resource for those interested in New Zealand hydrology and climatology. Similarly, I also find the manuscript to be well written and presented, and have only minor changes to suggest. These are as follows:

Introduction: The recency of its publication is probably the reason for this omission, but the ROBIN (Reference observatory of basins for international hydrological climate change detection) data set (Turner et al. 2025; https://doi.org/10.1038/s41597-025-04907-y) needs to be acknowledged here. As the name indicates, this is a global dataset of streamflow records that have minimal direct anthropogenic influence. Importantly, this includes 111 flow records for New Zealand. Although not diminishing the contribution that this new CAMELS-NZ dataset provides, it is important to note that another large streamflow dataset for NZ exists. Furthermore, it would be very helpful if an additional attribute could be added to this CAMELS dataset to indicate which of its records have passed the ROBIN standards around direct human influence.

Line 73 and elsewhere: it seems a bit odd for the paper to cite itself – surely this is unnecessary?

Line 87: Snelder & Biggs (2002) is not the best citation here, at least not in isolation. Although it has some relevance, it does not directly describe the nature of NZ gradients in climate, topography and geology, or the extent to which they are ‘remarkable’.

Lines 103-104: volcanic catchments might be typical of Pacific Islands, but alpine settings are not.

Line 114: Naming conventions. While Aotearoa is commonly added to New Zealand (i.e. Aotearoa New Zealand) for the name of the country, it is not the official name of the country. Contrastingly, the North and South Islands do have official names in Te Reo, according to the NZ Geographical Board: Te Ika-a-Māui and Te Waipounamu, respectively (https://gazetteer.linz.govt.nz/). Further, use of Aotearoa New Zealand vs just New Zealand in the manuscript is not consistent. Perhaps use the Te Reo plus English versions could be given at first use (for all NZ place names), then only English thereafter?

Line 140: Glacier formation is not quite the right term – this is a process that takes many years (and probably is not occurring anywhere in the world right now?). Perhaps glacier mass balance would be more appropriate.

Line 143: state the emissions scenario this projection corresponds to. Same comment for the following precipitation projections. Note that the King (2010) report has also been superseded by more recent assessments.

Line 249: Given that this paper describes a dataset, more specific information on estimation of PET would be helpful. Conventionally, the Priestley-Taylor equation uses an empirical constant to model the effects of vapour pressure deficit on evaporation, meaning that relative humidity data are not used. Similarly, net radiation (rather than temperature) data are used. However, the Clark et al. (2008) study that is cited here states that “radiation terms are estimated empirically”, followed by a citation to Shuttleworth (1993). Correspondingly, it would be helpful to state explicitly how PET is calculated for this data set, and thus how it differs from the conventional Priestley-Taylor method.

Line 224-225. It would be more accurate to state that whilst a very dense gauging network would be needed to capture the high spatial variability in rainfall for the mountainous regions of NZ, such a network does not currently exist.

Line 226-227. While the VCSN does provide these data, access to the VCSN is largely restricted behind a paywall (https://data.niwa.co.nz/products/vcsn-timeseries?_gl=1*1q6gqoh*_ga*MTg1MTUxMzY0OS4xNzUwODE2ODg1*_ga_4CXN46915J*czE3NTA4MTY4ODUkbzEkZzAkdDE3NTA4MTY4OTIkajUzJGwwJGgw ). If this new CAMELS-NZ dataset effectively bypasses that paywall for these catchment-average time series, this is good news and should be more clearly noted!

Some comment on the use of catchment average rainfall should be provided, particularly in regions of high rainfall gradients such as the eastern side of the Southern Alps Main Divide. In regions such as this where mean annual rainfall can drop by an order of magnitude across a catchment, how informative is catchment average rainfall? Perhaps an additional data set attribute quantifying this rainfall gradient would be helpful?

Finally with respect to the VCSN – previous studies have highlighted issues problems with these data with respect to interpolation across complex terrain using a sparse observational network, e.g. Tait and Macara (2014; https://doi.org/10.2307/26169743) and Tait et al. (2012; https://www.jstor.org/stable/43944886). These need to be acknowledged here.