How to solve a low recall and precision problem in an imbalanced dataset?

I'm working on a project related to prediction of power outage during extreme weather events (heatwave, drought, cold storm, etc). I've 5 yrs of data (hourly) in which 18% is positive class (where power outages are 1). I've previously worked on imbalanced datasets and such and achieved high precision and recall simultaneously but this time, it's just not happening.

I've tried, tree based, gradient, ensemble (boosting, hybrid, stacking), even TFT, tabnet, LSTM, Pytorch etc models when my initial framework didn't work. I've tried stratified and k-fold when 80-20 didn't work. I optimized the threshold, even manually checked the metrices at decision threshold 0.4 to 1.

It's a non-linear problem so I tinkered with feature eng when nothing worked. Spearman correlation doesn't rly work but I checked anyways, the top feature got 9.8% correlation with the target column, in mutual info score, the top metric got 13%.

But, after all these, the best metrices i got was from meta_LightGBM (5-Fold Stacking) that is 0.9145 ROC-AUC, 0.4372 PR-AUC, 52.09% precision, and 37.94% recall, and it was the best result I got after threshold tuning.

Can someone explain me what I'm doing wrong, or is there's another method/point I've to take into consideration?