Absolutely, but training is only once, being so efficient to make the actual forecast, you could have a forecast personally made for your own garden, which may be very different than a generic one covering hundreds of km². Then the about 90% accuracy will feel WAY more accurate.

I'm sure the model would need to be continuously updated to take in more recent weather data.

generalizing the world based on past observations - easy (with some lossyness)

I know people who spend their lives working on climate models, and none of them would say it's easy. And climate models are "generalizing the world based on past observations" plugged into some very complex physical models.

I'm not convinced you can ever get that resolution. There's a big difference between modeling the broad trends and trying to remove the uncertainty from a process that's inherently probabilistic.

It would be amazing if it could have a significant impact on spatial and temporal accuracy of things like rain.

That has to do with the forecast's grid size, along with some irreducibly complex and ill-conditioned physics that AI won't help with. The best general-forecast global models now have a 10km grid. Some specialist models go down to about 2km. So, depending on the size of your town, probably not fine-grained enough, though there are also point forecasts that take into account terrain, albedo and other fine-grained features and can be pretty accurate, especially when there are some good observation stations nearby so the forecast models can be continually trained based on actual data.

Ensemble forecasts are better yet, but you need to learn what they mean before using them. Not many people understand how to interpret probabilistic forecast data.

You'll also need more accurate remote sensor data (precipitation happens in a very narrow range of temperature, pressure and humidity), better observation data, better terrain models (microclimate is influenced by the interaction of terrain and the atmosphere). The forecast grid sizes used now are based on choosing the smallest grid size we can afford to compute that yields meaningful forecast data. Computational cost more than quadruples each time grid size halves. "More than" because altitude levels matter too, though for terrestrial forecasts, the ones near the ground matter a whole lot more than what's happening at 6km.

I had one time a couple weeks ago where I was scheduling jobs on Monday, we were supposed to be rained out Tuesday, light/scattered showers Wednesday, and heavy rain Thursday.

Actual results was no rain Tuesday, absolute downpour on Wednesday, and sunny Thursday and Friday.

Yeah, that’s pretty impressive. I wonder if you could apply the same philosophy in other areas too. Instead of training the model with data produced in a simulation, you could just feed it real world data instead. Like, if you gave a bunch of stress-strain data to a model, could you make better predictions about the behavior of physical structures, such as bridges and towers.

Physical simulations factor in a dozen or so weather conditions to predict outcomes.

Many more parameters than that.

Machine learning can track thousands of conditions

Scientists already know which ones are relevant. You're not going find any big surprises there with an AI. Shotgun-style factor analysis has already been done to death. The price of baked beans doesn't impact the wind direction in the Persian Gulf. It's OK to not consider it.

drawing connections not realized in physical models

Again, it's possible but unlikely. And you'd need an AI that could be queried to tell you what factors it considered, and most of them don't work that way right now.

Statistical models don't become more accurate because you throw irrelevant parameters at them. But that's how ML systems work.