Identifying and Accounting for trend/seasonality in Predictor Variables
Using time as an explanatory variable may be a good start. Then, depending on your model you may want to add interaction effects between time and your other variables. This will take into account change over time.

However, it will be a bit difficult to link evolution of the model to the evolution of some explanatory variable. If you know what variable evolve over time, you may just want to add time differentiated variables ($X_n - X_{n-1}$). This will give you a clearer view on what evolution is impacting the model output. Depending on your problem you might want to consider different time lags.