# Forecasting weather with time-dependent regression model

I am interested in making a time-dependent prediction model. The set up looks something like this:

Given weather data from the days Monday-Saturday I would like to predict the high point on Sunday. Now, this unfeasible since we only have a small number of data points. But I do have Mon-Sat weather data from $M$ different locations, say in California, where $M>300,000$. Part from the feature set includes location data. So I would like my model to have as input Mon-Sat weather data from any location in California, for example, Santa Monica, and as output a prediction of the high point. Given that I have $M$ locations to train on, I would like a model that takes into account areas that are similar to Santa Monica (other beaches and locations that are within a certain radius of Santa Monica) and ignores other locations like mountain/inland areas.

I have been searching for a model that fits this description but I haven't stumbled upon anything. Or does this training method simply not make sense? I have never done any time dependent models, so I wouldn't know how feasible this problem is. Thanks in advance for the help.