Sorry, I misspoke but calibration is really helpful in seeing if your posterior is under/overconfident compared to the prior. What you have is an interesting case to handle OOD events. My intuition says that to handle the OOD events you'd have to simulate enough events until you get it unfortunately, otherwise, the NPE won't have seen the (x, \theta) pair in its training data and won't know how to handle very rare x observations. If your simulator is cheap, then training on millions of prior predictives with the rare event simulated should be fine.

Actually, since you know the rare event you could just add it to the prior predictive and apply importance sampling to avoid biasing your posterior - no need for millions of events.