Is it that 'physics simulation' forecasting is best for short timescales (e.g. < 1 week in the uk), whilst ML can be better for estimating next year's weather s

Is it that 'physics simulation' forecasting is best for short timescales (e.g. < 1 week in the uk), whilst ML can be better for estimating next year's weather statistics in some particular place? (Because your butterfly effect means that simulating the actual weather out to 1 year 'accurately' is not possible). Presumably any short-term ML forecasting would use the physics simulation as it's main input, and then try to improve slightly on it (e.g. maybe you observe that in a particular small area, the rainfall is on average 10% more than the physics simulation, so your ML method would add 10%, giving you a slight forecast improvement)?