ERA5 daily min and max t2m on dec 2019 dataset do not contain same attributes than nov 2019

Dear support,

The following request to get daily max t2m over nov 2019 :

data = ct.catalogue.retrieve(
  'reanalysis-era5-single-levels',
  {
  'variable': 'maximum_2m_temperature_since_previous_post_processing',
  'format': 'netcdf',
  'product_type': 'reanalysis',
  'year': '2019',
  'month':['11'],
  'day':[
    '01','02','03',
    '04','05','06',
    '07','08','09',
    '10','11','12',
    '13','14','15',
    '16','17','18',
    '19','20','21',
    '22','23','24',
    '25','26','27',
    '28','29','30'
  ],
  'time':[
    '00:00','01:00','02:00',
    '03:00','04:00','05:00',
    '06:00','07:00','08:00',
    '09:00','10:00','11:00',
    '12:00','13:00','14:00',
    '15:00','16:00','17:00',
    '18:00','19:00','20:00',
    '21:00','22:00','23:00'
  ],
  'area' : "75/-25/25/45"
  }
)

print(data)

returns a dataset containing only one attribute called mx2t_NON_CDM_NON_CDM :

<xarray.DataArray 'mx2t_NON_CDM' (time: 720, lat: 201, lon: 281)>
dask.array<shape=(720, 201, 281), dtype=float32, chunksize=(48, 201, 281)>
Coordinates:
  * lon      (lon) float64 -25.0 -24.75 -24.5 -24.25 ... 44.25 44.5 44.75 45.0
  * lat      (lat) float64 25.0 25.25 25.5 25.75 26.0 ... 74.25 74.5 74.75 75.0
  * time     (time) datetime64[ns] 2019-11-01 ... 2019-11-30T23:00:00
Attributes:
    units:          K
    long_name:      Maximum temperature at 2 metres since previous post-proce...
    standard_name:  mx2t_NON_CDM_NON_CDM
    Conventions:    CF-1.6
    history:        2020-02-12 11:02:36 GMT by grib_to_netcdf-2.16.0: /opt/ec...
    institution:    ECMWF
    source:         ECMWF

But when I do the same request on dec 2019, I get a dataset containing 2 parameters mx2t_0001_NON_CDM_NON_CDM and mx2t_0005_NON_CDM_NON_CDM :

[<xarray.DataArray 'mx2t_0001_NON_CDM' (time: 744, lat: 201, lon: 281)>
dask.array<shape=(744, 201, 281), dtype=float32, chunksize=(48, 201, 281)>
Coordinates:
  * lon      (lon) float64 -25.0 -24.75 -24.5 -24.25 ... 44.25 44.5 44.75 45.0
  * lat      (lat) float64 25.0 25.25 25.5 25.75 26.0 ... 74.25 74.5 74.75 75.0
  * time     (time) datetime64[ns] 2019-12-01 ... 2019-12-31T23:00:00
Attributes:
    units:          K
    long_name:      Maximum temperature at 2 metres since previous post-proce...
    standard_name:  mx2t_0001_NON_CDM_NON_CDM
    Conventions:    CF-1.6
    history:        2020-02-12 10:41:07 GMT by grib_to_netcdf-2.16.0: /opt/ec...
    institution:    ECMWF
    source:         ECMWF, <xarray.DataArray 'mx2t_0005_NON_CDM' (time: 744, lat: 201, lon: 281)>
dask.array<shape=(744, 201, 281), dtype=float32, chunksize=(48, 201, 281)>
Coordinates:
  * lon      (lon) float64 -25.0 -24.75 -24.5 -24.25 ... 44.25 44.5 44.75 45.0
  * lat      (lat) float64 25.0 25.25 25.5 25.75 26.0 ... 74.25 74.5 74.75 75.0
  * time     (time) datetime64[ns] 2019-12-01 ... 2019-12-31T23:00:00
Attributes:
    units:          K
    long_name:      Maximum temperature at 2 metres since previous post-proce...
    standard_name:  mx2t_0005_NON_CDM_NON_CDM
    Conventions:    CF-1.6
    history:        2020-02-12 10:41:07 GMT by grib_to_netcdf-2.16.0: /opt/ec...
    institution:    ECMWF
    source:         ECMWF]

Here is the request for dec 2019 :

data = ct.catalogue.retrieve(
  'reanalysis-era5-single-levels',
  {
  'variable': 'maximum_2m_temperature_since_previous_post_processing',
  'format': 'netcdf',
  'product_type': 'reanalysis',
  'year': '2019',
  'month':['12'],
  'day':[
    '01','02','03',
    '04','05','06',
    '07','08','09',
    '10','11','12',
    '13','14','15',
    '16','17','18',
    '19','20','21',
    '22','23','24',
    '25','26','27',
    '28','29','30','31'
  ],
  'time':[
    '00:00','01:00','02:00',
    '03:00','04:00','05:00',
    '06:00','07:00','08:00',
    '09:00','10:00','11:00',
    '12:00','13:00','14:00',
    '15:00','16:00','17:00',
    '18:00','19:00','20:00',
    '21:00','22:00','23:00'
  ],
  'area' : "75/-25/25/45"
  }
)

print(data)

I also noticed the same behavior on minimum_2m_temperature_since_previous_post_processing on dec 2019.

Could you please tell me whether this is a normal behavior, and if so, which attribute to take into account between mx2t_001 and mx2t_005 ?

All the best

Julien