The authors conducted a thorough comparison of recurrent neural networks on NDVI prediction. The manuscript is well-written and easy to follow. It would be a good fit to be published in Nonlinear Processes in Geophysics. Here are several comments to be addressed before publication. 

Title. Considering that the models applied to all NDVI time series and extreme cases are analyzed for performance comparison, I would suggest removing “extreme” from the title to better reflect the broader application of the model 

Section 2.1. The models are trained separately for each site. Is it feasible to train a universal model with all the stations by incorporating some static features (such as elevation, latitude, longitude, etc.,)? There have been studies showing the LSTM model benefits from diverse training datasets. How might this approach impact conclusions, especially regarding whether gated models benefit more from augmented training data given their complex architectures? 

DL features. The input features need to be clarified, particularly mean temperature and sea level pressure. Are these area means over the European continent, or are they in-situ measurements? If the latter, how do the gridded dataset correlate with in-situ NDVI values? Additionally, clarify the input time window size for the model. 

Figure2, I believe subfigure a is for conventional RNN, LSTM and b is for ESN. The figure caption is mismatched. 

Figure4, is the black line for the real/target value? I would suggest adding it in the legend. 

Figure5, the legend color in the right panel is missing. Please revise it for completeness. 

Table 1. Clarify whether the standard deviation is derived from the 20 selected sites. 

I would suggest adding a comparison of training and inference speed of the selected networks for a more comprehensive evaluation. 

In general, the manuscript is well-written and organized. It would have an impactful contribution to literature. Here are my comments that need to be addressed.

I advise the authors to change the places where "temperature" is written to "air temperature". And “global radiation” to “global solar radiation”.

Figure 1 a: The scale and north arrow should be added to the map.

Description of Fig 1. A typo “radii” needs to be corrected.

L115-116: Did you have in situ NDVI measurements to compare MODIS values?

Figure 2: It would be good to show differences in network architectures.

Section 2.4: What is the ratio between training and testing data?

Figure 3e: Please explain the meaning of the red-shaded area here?

Section 2.6.1: Please add a statement indicating at which values these performance indicators work well.

L246: Please use another abbreviation for entropy-complexity. In line 117, EC stands for "eddy covariance".

Figure 5b: The legend should be corrected.

Appendix B: Please add computational resources (GPU, etc.)?

Data Availability section: You should add MODIS data source link.