The multimesh is interesting. Still, I bet the Fourier Neural Operator approach will prove superior.
Members of the same team (Sanchez-Gonzales, Battaglia) have already published multiple variations of this model, applied to other physical scenarios and lots of them proved to be dead ends.
My money is on the FNO approach, anyway, which for some reason is only given a brief reference.
To their credit DeepMind usually publishes extensive comparisons with previously published models. This time such a comparison is conspicuously missing.
Full disclosure: I think DeepMind often publish these bombastic headlines about their models which often don't live up to their hype, or at least that was my personal experience. They have a good PR team, anyway.
Pragmatically speaking, it doesn't really matter if one is better than the other, at least until there is a massive jump in forecast quality (e.g. advancing the Day 5 accuracy up to Day 3). In the real world, we would never take raw model guidance from _any_ source - the best forecasts invariably come from consensus systems that look across many different models. So it's good to have a diverse lineage of forecasting systems, as uncorrelated errors boost the performance of these consensus systems.
Full disclosure: I think DeepMind often publish these bombastic headlines about their models which often don't live up to their hype, or at least that was my personal experience. They have a good PR team, anyway.