Does this do what you want? (instead of the inner loops, j and jj)

comp0 = numpy.min((vv-precomp[...,0])**2+(hh-precomp[...,1])**2, axis=2) 

This can replace the inner loops, j and jj

comp0 = numpy.min((vv-precomp[...,0])**2+(hh-precomp[...,1])**2, axis=2) 

This may be a replacement for the whole loop, though all this indexing is stretching my mind a bit. (this creates a large intermediate array though)

comp = numpy.sum( numpy.min((VV1.reshape(-1,1,1,1) - precomp[numpy.newaxis,...,0])**2 +(HH1.reshape(-1,1,1,1) - precomp[numpy.newaxis,...,1])**2, axis=2), axis=0)