This study tests whether ML can be trusted for a unique challenge: modeling a world with stratospheric aerosol intervention (SAI)—a proposed way to cool the planet by reflecting sunlight with tiny particles in the upper atmosphere. We trained an ML model on past climate records and historical simulations, then used it to increase the resolution of coarse climate data for future scenarios, including SAI. We compare the ML results to traditional methods and check how much the choice of method matters. If ML proves reliable, this approach could make high-quality climate data easier to produce and freely available, helping communities and researchers worldwide plan for an uncertain future.