Most CAMS and C3S data is produced and archived not on a Cartesian grid (a two-dimensional rectangular grid), but on a reduced Gaussian grid - think of it as a globe with a series of evenly spaced data points along each parallel (line of constant latitude), and parallels spaced at slightly irregular intervals. Near the poles you have only a few data points along a parallel, but close to the equator you have many data points along a parallel.
When you download CAMS data, C3S data and other data from ECMWF, you can obtain the output data on its archived grid or on a Cartesian lat/long grid at a custom resolution.
You can specify a higher output resolution than the archived resolution, but the resulting data will not contain any more information than the original, it has merely been interpolated[1] to a higher resolution. This makes the output look smoother, but does not increase the accuracy or the precision of the data. However, if you choose to interpolate to a coarser resolution than the archived resolution you should be aware that the data can be aliased, unless care was taken to avoid this.
For ERA-Interim atmospheric data the point interval on the native Gaussian grid is about 0.75 degrees. You can specify a custom grid using the CDS API or using the MARS client (if you have access to it).
ERA-Interim Ocean-Wave data are natively stored on the wave model’s reduced 1.0 degrees latitude/longitude grid.
For ERA5 HRES atmospheric data the point interval on the native Gaussian grid is about 0.28 degrees. You can download ERA5 data using Python and specify a custom grid and resolution in your script. You should set the horizontal resolution to slightly lower than 0.28 degrees (about 30km), for example to 0.25 degrees, approximating the irregular grid spacing on the native Gaussian grid.
ERA5 HRES Ocean-Wave data are natively stored on the wave model’s reduced 0.36 degrees latitude/longitude grid.
[1] When data is interpolated, all continuous fields (e.g. precipitation, temperature) are interpolated by bilinear interpolation, and discrete fields (e.g. vegetation, precipitation type, soil type) and Wave 2D spectra are interpolated by nearest-neighbour. For more information about our grids and interpolations see in this presentation https://confluence.ecmwf.int/download/attachments/55122669/intro-interpolation-2016.pdf?api=v2
