SEEPS4All review

The authors present a new station-based dataset to support evaluation of precipitation forecasts with the SEEPS score and other verification metrics. This dataset and the corresponding software for verification are useful contributions and the paper is well written and concise. The datasets are well described and accessible, but I suggest to reorganize the data as detailed below.

The suggested changes to the datasets can be summarized as:

• Percentile as a dimension rather than variable name
• Time dimension for percentiles and other climatological quantities as month of year to avoid redundant repetition of values
• Re-chunk the data, the current chunk layout produces way too many very small chunks of around 10kB. Recommendations for chunk sizes vary, but are generally in the 10MB range.
• Also add the maximum (i.e. the 100^th percentile)

To facilitate working with the new data layout, the scripts should be adjusted to take into account the difference in time representation.

So instead of the representation as below:

```

>>> xr.open_zarr("obs_clim_tp24_2022_2024_ecad.zarr")

<xarray.Dataset> Size: 9GB

Dimensions:      (stnid: 10562, time: 1097)

Coordinates:

    elevation    (stnid) int64 84kB dask.array<chunksize=(10562,), meta=np.ndarray>

    lat          (stnid) float64 84kB dask.array<chunksize=(10562,), meta=np.ndarray>

    lon          (stnid) float64 84kB dask.array<chunksize=(10562,), meta=np.ndarray>

  * stnid        (stnid) int64 84kB 13 14 15 16 21 ... 27706 27707 27708 27710

  * time         (time) datetime64[ns] 9kB 2022-01-01 2022-01-02 ... 2025-01-01

Data variables: (12/100)

    observation  (time, stnid) float64 93MB dask.array<chunksize=(138, 1321), meta=np.ndarray>

    perc1        (time, stnid) float64 93MB dask.array<chunksize=(138, 1321), meta=np.ndarray>

    perc10       (time, stnid) float64 93MB dask.array<chunksize=(138, 1321), meta=np.ndarray>

    perc11       (time, stnid) float64 93MB dask.array<chunksize=(138, 1321), meta=np.ndarray>

    perc12       (time, stnid) float64 93MB dask.array<chunksize=(138, 1321), meta=np.ndarray>

    perc13       (time, stnid) float64 93MB dask.array<chunksize=(138, 1321), meta=np.ndarray>

    ...           ...

    perc98       (time, stnid) float64 93MB dask.array<chunksize=(138, 1321), meta=np.ndarray>

    perc99       (time, stnid) float64 93MB dask.array<chunksize=(138, 1321), meta=np.ndarray>

Attributes:

    description:  observations with climate percentiles from 1 to 99

    licence:      CC-BY-NC. See also https://knmi-ecad-assets-prd.s3.amazonaw...

    version:      1.0.0

```

I suggest the following:

```

>>> xr.open_zarr("obs_clim_tp24_2022_2024_ecad.zarr")

<xarray.Dataset> Size: 9GB

Dimensions:      (stnid: 10562, time: 1097, doy: 365)

Coordinates:

    elevation    (stnid) int64 84kB dask.array<chunksize=(10562,), meta=np.ndarray>

    lat          (stnid) float64 84kB dask.array<chunksize=(10562,), meta=np.ndarray>

    lon          (stnid) float64 84kB dask.array<chunksize=(10562,), meta=np.ndarray>

  * stnid        (stnid) int64 84kB 13 14 15 16 21 ... 27706 27707 27708 27710

  * time         (time) datetime64[ns] 9kB 2022-01-01 2022-01-02 ... 2025-01-01

  * month        (month) in64 1 2 3 ... 12

  * perc         (perc) int64 1 2 3 ... 100

Data variables:

    observation  (time, stnid) float64 93MB

    percentile   (month, stnid, perc) float64 …

Attributes:

    description:  observations with climate percentiles from 1 to 99

    licence:      CC-BY-NC. See also https://knmi-ecad-assets-prd.s3.amazonaw...

    version:      1.0.0

```

Similarly, the `obs_seeps_tp24_2022_2024_ecad.zarr` dataset could be reorganized in corresponding fashion.

## Minor Editorial Comments:

L13: recognizes meteorological data as high value data, …

L143: PSS

The manuscript introduces a new dataset focused on station-based precipitation over Europe, including climatological statistics that facilitate the computation of meaningful verification metrics, like the SSEPS. This dataset can be useful to verify precipitation forecast, not only from NWP but also forecasts obtained from ML methods. The paper is well written and the data is accessible as described in the manuscript.

Regarding the dataset, there is already an open discussion about reformatting the data. I also think that including the maximum (100^th percentile) is a good idea and agree with the change of dimensions.

Regarding the manuscript, I have some minor comments and clarifying questions:

• Line 68: The dataset includes observations from 2022 to 2024 coming from ECA&D. This is presented as a novelty (Line 60), but is this part of the dataset just a direct subset of ECA&D?
• Fig1: Information about the number of the plotted stations is appreciated.
• Lines 82-83: The study uses the blended dataset provided in ECA&D. Blending data has an impact into extreme values, that are smoothed. Please, provide a comment about the impact this blending might have into the extreme cases of the provided dataset.