  We consider the problem of iteratively solving large and sparse double
saddle-point systems arising from the stationary Stokes-Darcy equations in two
dimensions, discretized by the Marker-and-Cell (MAC) finite difference method.
We analyze the eigenvalue distribution of a few ideal block preconditioners. We
then derive practical preconditioners that are based on approximations of Schur
complements that arise in a block decomposition of the double saddle-point
matrix. We show that including the interface conditions in the preconditioners
is key in the pursuit of scalability. Numerical results show good convergence
behavior of our preconditioned GMRES solver and demonstrate robustness of the
proposed preconditioner with respect to the physical parameters of the problem.
