I have two nc files:

WATCH data:

> watch_Tair:

     2 variables (excluding dimension variables):
        int timestp[tstep]   
            title: time steps
            units: time steps (days) since 2018-01-01 00:00:00
            long_name: time steps (days) since start of month
        float Tair[lon,lat,tstep]   
            title: Tair average
            units: K
            long_name: Average Near surface air temperature 
 at 2 m at time stamp
            actual_max: 314.173614501953
            actual_min: 212.381393432617
            _FillValue: 1.00000002004088e+20

     4 dimensions:
        lon  Size:720 
            title: Longitude
            units: grid box centre degrees_east
            actual_max: 179.75
            actual_min: -179.75
        lat  Size:360 
            title: Latitude
            units: grid box centre degrees_north
            actual_max: 89.75
            actual_min: -89.75
        tstep  Size:31   *** is unlimited *** (no dimvar)
        day  Size:31 
            title: day
            units: integer
            long_name: day of the month

ERA5 data:

> era5_Tair:

     1 variables (excluding dimension variables):
        short t2m[longitude,latitude,time]   
            scale_factor: 0.00169511169020999
            add_offset: 265.715689919741
            _FillValue: -32767
            missing_value: -32767
            units: K
            long_name: 2 metre temperature

     3 dimensions:
        longitude  Size:3600 
            units: degrees_east
            long_name: longitude
        latitude  Size:1801 
            units: degrees_north
            long_name: latitude
        time  Size:744 
            units: hours since 1900-01-01 00:00:00.0
            long_name: time
            calendar: gregorian

The aim is for ERA5 to match WATCH data structure.
I do some pre-processing on the ERA5 file to get the same spatial and temporal aggregation as for WATCH data:

#*Daily aggregation*
era5_Tair_daily <- tapp(era5_Tair, "days", mean) 

#`Spatial aggregation`
era5_temp_resampled <- terra::resample(era5_Tair_daily, watch_Tair, method = "bilinear")

Then, I save it back as a netcd file:

writeCDF(era5_temp_resampled, filename = "./era5_temp.nc", varname = "Tair", unit="K", zname="time", overwrite = TRUE)

> era5_temp_resample:

     2 variables (excluding dimension variables):
        float Tair[longitude,latitude,time]   (Contiguous storage)  
            units: K
            _FillValue: -1.17549402418441e+38
            long_name: Average Near surface air temperature at 2 m at time stamp
            grid_mapping: crs
        int crs[]   (Contiguous storage)  
            crs_wkt: GEOGCRS["unknown",    DATUM["World Geodetic System 1984",        ELLIPSOID["WGS 84",6378137,298.257223563,            LENGTHUNIT["metre",1]],        ID["EPSG",6326]],    PRIMEM["Greenwich",0,        ANGLEUNIT["degree",0.0174532925199433],        ID["EPSG",8901]],    CS[ellipsoidal,2],        AXIS["longitude",east,            ORDER[1],            ANGLEUNIT["degree",0.0174532925199433,                ID["EPSG",9122]]],        AXIS["latitude",north,            ORDER[2],            ANGLEUNIT["degree",0.0174532925199433,                ID["EPSG",9122]]]]
            spatial_ref: GEOGCRS["unknown",    DATUM["World Geodetic System 1984",        ELLIPSOID["WGS 84",6378137,298.257223563,            LENGTHUNIT["metre",1]],        ID["EPSG",6326]],    PRIMEM["Greenwich",0,        ANGLEUNIT["degree",0.0174532925199433],        ID["EPSG",8901]],    CS[ellipsoidal,2],        AXIS["longitude",east,            ORDER[1],            ANGLEUNIT["degree",0.0174532925199433,                ID["EPSG",9122]]],        AXIS["latitude",north,            ORDER[2],            ANGLEUNIT["degree",0.0174532925199433,                ID["EPSG",9122]]]]
            proj4: +proj=longlat +datum=WGS84 +no_defs
            geotransform: -80 0.5 0 15 0 -0.5

     3 dimensions:
        longitude  Size:100 
            units: degrees_east
            long_name: longitude
        latitude  Size:70 
            units: degrees_north
            long_name: latitude
        time  Size:31 
            units: days since 1970-1-1
            long_name: time
            calendar: standard

However, in the watch data, the nc file has as variables Tair + timestp and ERA5 (Tair + crs). How can I get the exact same structure?