This study proposes using spatial and temporal information from multiple meteorological variables to improve precipitation nowcasting. The authors develop a dual encoder–decoder ConvLSTM architecture based on the spatio-temporal information transformation framework, consisting of a spatio-temporal converter for precipitation prediction and a temporal-spatial converter that provides an inverse mapping to promote spatio-temporal consistency.

The paper is generally well organised. The model architecture and loss function are clearly described, and the experiments are well designed to evaluate the performance of the proposed model. The results are presented clearly, with effective illustrations and descriptions.

General comments

1. Introduction and Related Work sections: These sections are somewhat descriptive and read more like a listing of previous studies. The manuscript would benefit from additional synthesis and transition and summary sentences. This would help guide readers more effectively and better motivate the proposed approach.
2. Training with precipitation events only: At line 259, the authors mention that events with no rainfall are excluded from training. This choice requires further discussion. Although including many no-rain samples may lead to class imbalance, completely removing them may bias the training distribution and may require an additional strategy to handle rainfall occurrence and no-rain situations in operational applications.
3. Training with interpolated data: Table 2 indicates that training with interpolated data slightly outperforms training with the original-resolution data. However, the stated motivation for interpolation is reduced training complexity, suggesting an efficiency-driven rather than accuracy-driven choice. Since the authors also conducted experiments at the original resolution, training without interpolation appears feasible. If efficiency is the main motivation, the authors could provide a direct comparison of computational cost, such as training time and memory usage, between the interpolated and original-resolution settings.
4. Performance of the proposed model: The conclusion drawn in lines 324–325 could be too strong. The experimental results show that the proposed architecture performs better than other models under the specific experimental setting (e.g., the chosen dataset). Do authors think the proposed architecture is generally superior beyond the tested experimental conditions? For operational applications, a fair comparison between different models should allow each model to use its preferred input data, loss function, and training strategy. The forecast skill should then be evaluated against independent observational sources that are separate from the data used to train any of the models.

Specific comments:

1. Line 29: The phrase “… recent observational data more effectively capture precipitation at the next time step…” reads a bit confusing.
2. Line 51: As the acronym SOTA only appears once in the manuscript, the full name should be used instead.
3. Line 63: The full name of LSTM should be given for the first time.
4. Lines 137–138: The paper “Predicting future dynamics from short-term time series using an Anticipated Learning Machine” (https://doi.org/10.1093/nsr/nwaa025) seems relevant to the discussion of using spatio-temporal information. 
5. Lines 257-258: Should the contribution of lightning data to precipitation forecasting be case-dependent, for example depending on rainfall type, convective intensity, and the availability of other data types?
6. Line 266: What is the spatial resolution after interpolation?
7. Line 321: I wonder whether the same loss functions were used across all models.
8. Line 330: The word “efficient” usually implies reduced computational cost. However, in this context, the authors seem to be referring mainly to the accuracy or effectiveness of the model in representing uncertainty.
9. Figures 5 and 6: The proposed method appears to be clearly separated from the other methods in terms of forecast skill, while the remaining methods seem to form a relatively similar group. Could the authors discuss the main factors contributing to this separation?
10. Figure 6: Could the authors explain the jumps in the PastNet results at small spatial scales, shown by the purple lines?
11. Line 417: The phrase “Overall, STI-DEDN is more applicable in large-scale extreme precipitation events” could be confusing. Forecasting extreme precipitation more accurately at larger spatial scales is not the same as forecasting large-scale extreme precipitation events.
12. Figures 5, 7 and 9: Rainymotion appears to preserve more small-scale texture than other models. Could the authors discuss the reason for this?
13. Section 5.4.2: The meaning of “removing the STI framework” could be defined more explicitly. Does this refer specifically to removing the dual spatio-temporal and temporal-spatial converter structure?

Technical corrections

1. There is missing space before “(” in several places, for example in lines 79, 85, 89 and 172.
2. Please use lowercase “on” in titles.
3. Please ensure consistent capitalization across all titles.
4. Line 261: There appears to be a duplicated period at the end of the sentence.