Contour plot interpolation recommendation

I am not sure if my question is on topic or not and if not please let me know. I have regularly spaced gridded data(output of a weather forecast simulation software) and I have latitude and longitudes as 2D arrays along with geopotential height(2D array). The data is in netCDF or GRIB format. Ultimately I am looking to make a contour plot of the heights data and I am looking to use Python's scipy and matplotlib package. I initially need to do an interpolation of the various data points. From the scipy package what would be the best interpolation function that I should choose for my use case which has structured grid data ? Is something like the linked API ideally suited for me ? Initially I went ahead and tried griddata but this seems for unstructured grid data.

Regular Grid Interpolation

If your data are regularly spaced, you do not need an interpolation procedure, you can directly plot a contour through a contour or a pcolor command. That why I don't understand why you need to initially interpolate.
Contouring algorithms often use marching cube method (and its variations) to render a contour plot. This kind of algorithm only work with a structured grid. For an unstructured grid, routines likes griddata perform a Delaunay triangulation or similar and indeed do an interpolation to recover data on an structured grid where the contouring algorithm can be applied.