Can a complex interaction term mean more than what it's composed of?
What you describe as "complex interaction term" is typically referred to as composite variable.  Interaction terms are products of two or more variables.  You also use terms like "effect" and "meaning", which in more technical sense, seem to refer to issues of unbiasedness, accuracy, and interpretation. 

If a composite variable is a linear combination of other variables (e.g., their sum), then including that variable over its components will not change the predictive power of a linear regression model, though it will change the interpretation of regression coefficient(s).  If it is a non-linear combination, such as the case with your WCI variable, then you will obtain different predictions in a linear regression model.  If you are interested in interpreting the effect of WCI on your dependent/outcome variable (or if you believe it lies in the causal pathway and affects the interpretation of another variable of interest), then you should include WCI in the model.  The interpretation will be standard for regression models, i.e., by how much the value of dependent variable is expected to change for a one-unit increase in the value of WCI, holding other variables constant.  If, however, you are interested in the effect of wind velocity or temperature on the outcome, then you will have a hard time interpreting the results with WCI used as explanatory variable.  Including interaction terms or composite variables can also lessen the precision of your coefficient estimates, just like it does when you have multicollinearity.

In purely predictive (machine learning) models, you are most interested in the predictive power of your model, not in the interpretation of variables.  Those models readily accept "engineered" variables (hence, "feature engineering" is super important for models like random forests, though not for deep learning).  Accuracy will depend on a lot of factors, but using composite variables can certainly help.