The manuscript is of good quality and of high relevance. However, the methods are not yet described in sufficient detail to finally judge the value of the results. In the supplementary document, I provide more details of the points that I am missing in the method section and other points that should be addressed.

The authors evaluate different transformer models under different scenarios (i.e., regression, data integration, and autoregression) in comparison with benchmark models, LSTM networks and DLinear, using various hydrologic datasets. In addition, they compare the performance of LSTM networks with pre-trained LLM in the zero-shot forecasting for autoregression tasks. They show that LSTM networks outperform transformers in regression and data integration tasks, and attention-based methods surpass LSTM networks in autoregression and zero-shot forecasting. The paper is well written, with deep discussion. I have minor comments below.

For boarder audiences with minor ML/DL backgrounds, it would be helpful to provide brief introductions of the DL models used in the manuscript. The information can be provided in the SI if there are limited spaces in the main text. Table 1 provides the main features of different variants of transformers. Since ML/DL has many unique terms, simply providing the names of features does not really help understand their differences. Maybe the authors can adapt the table with more general features, such as “Trained on time series”.

In Section 2.3-attention models, it is better to mention that the authors use pre-trained LLM for zero-shot forecasting. This information come out in Section 2.4.5. In addition, for zero-shot forecasting, the authors only compare DL model performance in autoregression tasks, and state that LSTM underperforms pre-trained LLMs in zero-shot forecasting. How about regression and data integration tasks? I would expect that the LLMs cannot really understand the relationship between input and output without fine-tuning.

Specific comments:

Some figures for equations are burr, like Equation 5.

Equation 6: stating that r is Pearson’s correlation coefficient.

Equation 8: I think the equation is wrong. It should be (RMSE^2-Bias^2)^0.5.