Hello, this could be a very impactful finding if the ANN model generalizes well to weather conditions that it hasn't seen. I notice though in your Supplemental S2 section you describe randomly splitting the IAGOS waypoints into train/validation/test sets. Doing your cross validation in this way has a risk that your ANN model is overfitting. This is likely not a problem if you restrict your usage of the trained ANN to retrospective studies where the model inference is only applied to ERA5 data in the same times/places the model was trained on. However, if you attempted to apply an ANN trained in this way to a forecast of weather which has not happened in the real world yet, you would likely see a drop in metrics. To report metrics that are predictive of how the model will perform when applied to a weather forecast, it is best practice to train the ANN on an archived weather forecast (eg, ECMWF HRES) and use a chronological cross validation split: ie, the train set is comprised of data from time periods that are disjoint from the time periods used for the validation and test sets. For example, don't include in your validation/test sets any data from days that were included in your training set. This type of cross validation setup avoids the risk of the ANN 'memorizing' specific datapoints from the training set which are effectively also present in the validation/test sets, in a way that would not be the case when you apply the ANN to real weather forecast data. This is especially a concern here given the IAGOS waypoints occur once every 4 seconds and so adjacent datapoints (having extremely similar model inputs and target outputs) will frequently be randomly split across the train/test boundary. With the current cross validation setup, the impact of this model still seems strong, but limited to use in retrospective analyses.

In addition to the ETS, it would be nice to have the full contingency table used to evaluate the model (e.g. for the cases in Table 2), so that other metrics could be computed if readers are interested

The authors write that "This collocation of model meteorological variables and measured humidity values from the year 2020 comprises 3.99 million individual data points, from which 80%, 10%, and 10% are randomly selected for training, testing during the model development, and validating the ANNs, respectively." Presumably they refer to the full-resolution (i.e., 4s sampling) IAGOS data. Given the high sampling frequency, and if our understanding is correct, there is a strong autocorrelation in the data. Thus randomly selecting the training, testing and validation datasets implies that very similar conditions to those of the testing and validation datasets have been met in the training dataset. There is a well-known risk that this inflates artificially the model performance (see e.g. https://doi.org/10.1016/j.ophoto.2022.100018). At the very least the authors should select separate IAGOS flights in their training, testing and validation datasets. Even better they should consider dates that are at least one day apart for a given region. 

It is unclear if the testing or validation dataset are used in Section 4 as text on line 185 and in Section 4 appears contradictory. In any case we would recommend the testing and validation datasets to be temporally disjoint from the training dataset at every location.

I apologize for posting my piecemeal comments. I have two more questions.

Line 268: it is not clear what the 56 inputs consist of.  8 times 2 times 5 makes 80 so presumably some variables have fewer times or pressure levels. Which ones? Table 1 does not really clarify this. Could the authors provide more details? 

ANN: were the input and output data normalized and how? I could not find the information neither in the main text nor in the Appendix (sorry if I missed it). Section 3.3 presents an ablation study where one ERA5 variable is set to zero at a time, so I assume the data have been centred (as usual practice is to set the variable to its mean value).