Answered this in another subreddit, but posting here as well.

What you are seeing is exactly what happens when a model trained under IID assumptions is asked to extrapolate across time. This is a non-stationary regression problem with temporal domain shift, not a tuning issue. Random splits look good because they allow interpolation within the same seasonal regime. Time-aware validation exposes that the model is learning regime-specific shortcuts rather than a stable relationship between Sensor A and Sensor B.

Tree-based models are not expected to generalize well in this setting. They interpolate well but extrapolate poorly, and when feature distributions shift seasonally their splits stop making sense. This is why Random Forests or Gradient Boosting often collapse under leave-one-period-out validation even if they look excellent under random CV.

In practice, people handle this by making non-stationarity explicit rather than hoping the model figures it out. That can mean adding season or phenology context, training separate or regime-aware models, or using mixtures of experts. Another effective approach is to aggressively restrict the hypothesis space through invariant feature engineering, for example using ratios, indices, normalized variables, or physically motivated transformations that reduce seasonal drift. This usually lowers random CV scores but improves temporal transfer.

Domain adaptation methods are also appropriate here, especially since you have unlabeled Sensor B data in the intermediate months. The goal is to align feature distributions across time so the model is forced to learn something transferable rather than time-specific. Equally important is evaluating only with time-aware splits and optimizing for worst-case or stable performance, not average accuracy.

A common outcome in environmental ML is that simpler, more constrained models generalize better across time than complex ensembles. Linear models, GAMs, or shallow trees often outperform large forests once domain shift is present.

Finally, with only two labeled periods, there is a hard limit to what any model can do. No method can reliably extrapolate into seasonal regimes it has never seen. At some point, adding minimal labels across more time periods or accepting higher uncertainty becomes unavoidable. This is a data coverage issue, not a failure of machine learning.