I am a bit confused about advantages of mixed models in regard to predictive modelling. Since predictive models are usually meant to predict values of previously unknown observations then it seems obvious to me that the only way a mixed model may be useful is through its ability to provide population-level predictions (that is without adding any random effects). However, the problem is that so far in my experience population-level predictions based on mixed models are significantly worse than predictions based on standard regression models with fixed effects only.
So what is the point of mixed models in regard to prediction problems?
EDIT. The problem is the following: I fitted a mixed model (with both fixed and random effects) and standard linear model with fixed effects only. When I do cross-validation I get a following hierarchy of predictive accuracy: 1) mixed models when predicting using fixed and random effects (but this works of course only for observations with known levels of random effects variables, so this predictive approach seems not to be suitable for real predictive applications!); 2) standard linear model; 3) mixed model when using population-level predictions (so with random effects thrown out). Thus, the only difference between standard linear model and mixed model are somewhat different value of coefficients due to different estimation methods (i.e. there are the same effects/predictors in both models, but they have different associated coefficients).
So my confusion boils down to a question, why would I ever use a mixed model as a predictive model, since using mixed model to generate population-level predictions seems to be an inferior strategy in comparison to a standard linear model.