Well since nobody has come with any help I'll post my own solution. As hinted starting from the solution on Interpolation in vector-valued multi-variate function and with little more research on LinearNDinterpolation from Extrapolate with LinearNDInterpolator and basically here Python 4D linear interpolation on a rectangular grid (I missed this in my first search) I adapted it to my case in a quite straightforward way.
points = np.array((a1, a2)).T
values = np.array((models))
ip = interpolate.LinearNDInterpolator(points, values)
p = [A1_new, A2_new]
new_model = ip([point])[0]
Where a1, a2 are the arrays with the grid points of each of the parameters, for example in a 2x3 grid:
a1= [1.1, 1.1, 1.1, 2.2, 2.2, 2.2]
a2= [3.3, 4.4, 5.5, 3.3, 4.4, 5.5]
and a plot as example (of my models):
With that I can call the "function" ip at any point of the grid that I need, this is also easily extended for higher dimensions new_model = ip(A1', A2', ...)
.
Although I have not check the scalability of it with time (if it's linear with N or most probably goes with the power of N.)
I hope this can be of some help to others.