AI models are trained by analyzing vast amounts of data from various sources, such as weather satellites, ground-based sensors, and historical weather records.
AI can predict anything as long as you have enough data. However, regarding rain, accuracy might not be perfect as it has much more parameters not only depending on seasonal and weekly factors but also human interaction with nature. To achieve super high results, you might need a super-computer and vast amounts of data. Yet, I think 70-80% accuracy is probably possible with laptops.
P.S. only tried few models back in university days, so I might be missing something, so don't take it as accurate statement but rather as conversation.
Through machine learning techniques such as neural networks, regression trees or Bayesian networks. Above all, for short-term prediction. The challenge will be to train these algorithms with the most significant variables per study area. And above all, have sufficient historical data on both the variable to be predicted (in this case rain) and the variables that you are going to use as characteristics (for example, atmospheric instability indices or other meteorological variables).
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