It's still not as good as actually new data. The individual model may not overfit the existing data, but the whole system of researchers trying lots of models, choosing the best ones, trying different hyperparameters etc. easily can.
In principle they trained on data up to 2017, validated (tried different hyper parameters) on data from 2018, and published results on data from 2019...
yeah, but weather patterns data isn't different now than 10 years ago, right? right? ...
Overfitting is always bad by definition. The model learns meaningless noise that happens to help getting good results when applied to old data (be it due to trained weights, hyperparameters, whatever) but doesn't help at all on new data.