  This study investigates how conditional normalizing flows can be applied to
remote sensing data products in climate science for spatio-temporal prediction.
The method is chosen due to its desired properties such as exact likelihood
computation, predictive uncertainty estimation and efficient inference and
sampling which facilitates faster exploration of climate scenarios.
Experimental findings reveal that the conditioned spatio-temporal flow
surpasses both deterministic and stochastic baselines in prolonged rollout
scenarios. It exhibits stable extrapolation beyond the training time horizon
for extended rollout durations. These findings contribute valuable insights to
the field of spatio-temporal modeling, with potential applications spanning
diverse scientific disciplines.
