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They are all wrong. Historically they've overpredicted warming, and then the models are updated to fit the (new) data better. They perform worse in areas with less data, like polar regions.

To forestall knee-jerk downvotes: I'm not saying climate change isn't real or that anthropogenic global warming doesn't exist. I'm saying the models are not yet developed enough to predict very accurately. Early heliocentric models made poor predictions too because they assumed circular rather than elliptical orbits, they were still more "right" than geocentric models.

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In fact, climate models going back over fifty years have performed quite well; surprisingly so:

https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/201...

https://www.theguardian.com/environment/climate-consensus-97...

It depends on how you define "quite well". They are definitely directionally correct and in the right ballpark. This is a politically sensitive research area so the researchers are incentivized to make strong claims that you wouldn't make about, say, predicting sports outcomes or the stock market.

The first link you posted is kind of punting on the hard part by saying that the reason the models overpredict warming is because CO2 didn't rise as much as they expected so if you put the actual observed CO2 concentration in then the temperature prediction comes out closer to what was observed. But the CO2 concentration is a parameter of the model, so they didn't capture its dynamics properly and then had to retroactively change it to get the observed data.

Again, I want to reiterate that I'm not disputing the process of climate change or saying it's not a problem. I'm saying that modeling it is hard and historically the models have overestimated warming.

That doesn’t seem like a fair criticism. Most climate models are physics models. They can’t know how much CO2 people are going to add to the atmosphere. But if you tell them how we altered the composition of the atmosphere, they predict the temperature change correctly. That’s what I mean by good performance.

Part of the job of the modeler is to make good decisions about the parameters and their uncertainties. If they are systematically overestimating CO2 and getting high predictions as a result then they aren't modeling properly, unless there are exogenous reasons why CO2 is lower than expected. Which might be possible for all I know (Global financial crisis maybe? China growth lower than expected?) but the dynamics of CO2 are part of the model they are using for prediction so they can't just punt on it by saying they didn't know what CO2 would be. Prediction's hard, especially about the future, as the saying goes.

Can you suggest good places to look for more info on this?

I have tried a few times to find info on the accuracy of these models and couldn't find much. And most models seem to be closed source.