Thank you for your thoughtful and constructive feedback. We appreciate the time you have taken to review our manuscript. Below is a point-by-point response to your comments:

1. Clear Definition of Objective:

While we explained what our goal is with the electricity demand models in the last part of our manuscript (Perspective), we recognize the importance of clearly defining the goal of our models from the beginning of the article. In the next version, we will add a dedicated paragraph to the introduction that explicitly states our primary goal. Our objective is to project future electricity demand using models that incorporate IPCC projections of climate variables (CMIP6 models) under different scenarios (Shared Socioeconomic Pathways). This approach allows us to identify potential trends in electricity demand, with a particular focus on the impact of climate change on heating and cooling demand. Finally, we attempt to calculate the associated CO2 emissions to address the question of whether climate change is significantly influencing electricity demand, potentially triggering a feedback loop on global temperatures.

1. Incorporating Additional References:

Thank you for suggesting additional references. We plan to expand our bibliography, particularly in the introduction, to provide a more comprehensive overview of our study's contribution in the revised version of the article. While our initial literature review focused primarily on machine learning load forecasting, we recognize the importance of including references on the relationship between climate/weather variability and electricity demand.

The references you suggested include references to a multi-linear model for demand simulation (Bloomfield et al., 2016; Bloomfield et al., 2019). We intend to include these references to expand our literature review. In addition, we will include Dubus et al, 2021 (https://doi.org/10.31223/X5MM06) on the C3S Energy service. We also plan to include two additional papers that specifically address the use of GAM models and boosting approaches for load forecasting: Fan and Hyndman, 2012 (https://doi.org/10.1109/PESGM.2012.6345304) and Hong et al, 2016 (https://doi.org/10.1016/j.ijforecast.2016.02.001). These latter papers provide evidence for the superiority of GAM and nonparametric approaches in the context of load forecasting. These additional references also enable us to address your subsequent comment regarding the necessity of a simple baseline model.

1. Simple Baseline Model:

Based on these references, we argue that the existing evidence demonstrates the superior performance of GAM and machine learning approaches compared to multi-linear models. This leads us to question the necessity of including a benchmark study in our article to establish the added value of nonparametric approaches. However, recognizing the importance of a comprehensive evaluation, we are open to including a benchmark section in the revised version of the article if you deem it necessary.

We also want to emphasize that our chosen approach, particularly with GAMs, is designed for flexibility and ease of inclusion of multiple variables. The interpretability of GAMs, represented as a sum of spline functions, allows for a straightforward understanding of each explanatory feature. For other machine learning models, we use interpretability methods to automatically identify significant variables. This, in turn, facilitates the exclusion of climatic factors that do not contribute meaningfully to our electricity demand forecasting studies. In addition, our approach minimizes the need for manual tuning compared to multi-linear models, providing efficiency and automation.

1. Terminology Adjustment:

We acknowledge your suggestion to change "daily power demand simulation" to "simulate daily power generation" and will incorporate this adjustment in the revised version.

1. TOY Variables:

We argue that the coding of TOY variables is correct for GAM models because it automatically generates cyclic splines. While sinusoidal functions could improve accuracy for other machine learning models, our current approach with tree-based models efficiently handles these variables thanks to its ability to easily generate thresholds, thus allowing the first and last days of the year to be placed in the same category.

Thank you for your thorough review and positive feedback on our paper. We appreciate your valuable insights and would like to address the additional comments you provided. Here is a point-by-point response:

1. Citation of previous similar papers

As mentioned in our response to the previous comment, we are committed to improving the literature review in the Introduction to better contextualize our work and highlight its contribution. Specifically, we will include a paragraph illustrating how nonparametric models streamline automation and application across countries. To our knowledge, there is a gap in global studies utilizing such nonparametric methods. Existing data-driven studies rely on multilinear models, as exemplified in the article you cited.

1. Model Validation with extremes

In terms of evaluating the performance of our model under extreme conditions, we acknowledge its current limitations in dealing with extremes. In our future studies, we plan to explore specialized models designed for extreme conditions. Recent works, including Fasiolo et al. (2021, https://doi.org/10.1080/01621459.2020.1725521), Velthoen et al. (2022, https://doi.org/10.48550/arXiv.2103.00808), and Gnecco et al. (2023, https://doi.org/10.48550/arXiv.2201.12865), have explored novel machine learning approaches to improve the understanding and prediction of extreme quantiles. We will discuss these limitations and potential advances in the Discussion section (5.2), and explicitly mention in the Perspectives section that we aim to develop models tailored to extremes.

1. Accounting for cooling and heating technologies

We are currently working on multi-country modeling to address data gaps, particularly in regions with limited air conditioning infrastructure, such as Europe compared to Japan or the US. This involves taking climate-energy demand relationships from one country and applying them to another. For example, we plan to simulate European electricity demand using the electricity-demand-climate relationship observed in Japan above the cooling threshold. In addition, our ongoing project involves refining the projection aspect by incorporating finer spatial resolution data. This includes integrating more socio-economic predictors, including demographic characteristics and building characteristics such as insulation and exposure.