This study investigates how conditional normalizing flows can be applied to remote sensing data products in climate science. 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 and handling missing data. Experiments show the method is able to capture spatio-temporal correlations and extrapolates well beyond the training time horizon. These insights contribute to the broader field of spatio-temporal modeling, offering potential applications across diverse scientific disciplines.