Mixed Effects model variables of different temporal resolution

I am analyzing a study nitrous oxide and carbon dioxide emissions influenced by precipitation and ecosystem nitrogen dynamics.

I have six data sets with one response variable measured at hourly resolution and four predictor variables; two at hourly resolution, one discrete (single event), and one measured monthly or biweekly. What I am wondering is what is the best way to model the influence of predictor variables of different resolution on a single response variable.

To give you a sense of my dataset: my response variable and high resolution predictors have 10,000+ measurements, 6 single event predictor measurements, and ~50+ monthly to biweekly predictor measurements. Any recommendations on how to build this model?