Consumer weather prediction isn't about being right. It's about pleasing the customer by appearing to be helpful. Which often means exaggerating the chances of abnormal weather, so if it happens you can be a hero.
Real prediction is boring.
E.g. Darwin in Australia's tropics - persistence forecasting (as you describe above, just predicting the weather the day before) does very well on a metric like 'mean absolute error'. But has no practical skill at forecasting a severe tropical cyclone (aka hurricane/typhoon)! Many are willing to accept some level of false positives and a higher mean absolute error, because the cost of a surprise cyclone is so devestating.
I imagine using the previous day would have a much lower skill score in more variable climes.
I also couldn't find a link to this, and if you have one I'm interested in reading more.