Aviation impacts climate by forming contrails that trap heat and can persist for hours at cruising altitudes. Forecasting these humid regions is difficult, as satellites lack accuracy, aircraft data are limited, and ERA5 reanalysis has random errors. This study presents a hybrid machine learning method that corrects ERA5 with aircraft data, using decision trees in dry air and neural networks in humid air. It improves relative humidity predictions, especially in the lower stratosphere.