Performance Evaluation of CMIP6 Climate Model Projections for Precipitation and Temperature in the Upper Blue Nile Basin, Ethiopia

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper makes a valuable contribution to understanding the performance of CMIP6 climate models in projecting climate scenarios in the specific geographic location (Upper Blue Nile Basin, Northwestern Ethiopia), addressing an urgent and important topic.

The paper provides a detailed study about the impacts of climate change in Ethiopia identifying the vulnerabilities of farmers.

Some little consideration on the text:

- line 130 "The Pearson correlation...." could be better to put in bold like the other methods described

- line 166 there are different text size, is intentional?

- from line 238 to line 243 there are some repetitions, i.e "....values of r=0.91, r = 0.91, r=0.91 and R2 =0.83, R^2 = 0.83, R2=0.83....", the same in the lines between 252 and 255., and also in the lines between 266 and 269 with different text size

 

- authors should check the numbering of figures and tables, e.g. line 284 "...(Sub.1)..." this is Figure S1, the same in line 294. In line 333 the figure is not 6 but 5, in line 426 Sub 2 is Table S1,........

 

The conclusion succinctly summarizes the study's main findings may be perhaps it might be useful to add some advice about adaptation measures, with specific examples of such practices relevant to local agricultural practices or governance frameworks.

Author Response

Response to reviewers’ comments (climate-3172478)

We would like to thank the editor and the reviewers for their constructive and valuable comments, and for providing us an opportunity to revise our manuscript. We also appreciate your efforts and time to improve the manuscript. We have tried to address all the comments, questions and made the necessary changes to improve our revised manuscript. Our detailed responses to these comments are provided below.

We have numbered all the comments for ease of cross-referencing. The blue-coloured text in the revised manuscript shows the changes done in track change mode to our original submission. The line numbers refer to the track changed revised manuscript. The italic text in responses below shows the new text added/edited in response to the reviewers’ comments in the revised manuscript. 

Editor’s comments

1. Please download the latest version of the manuscript for revision. Your original submission may have been changed.

Response:

We thank the editor for providing us an opportunity to respond to the reviewers’ comments and revise our manuscript accordingly. As suggested, we have downloaded and carefully addressed all the reviewers' comments that have helped us to substantially improve the quality of our manuscript. 

2. If there has been a change in the authorship during revisions of your paper, please download the "Authorship Change Form" to provide details of the change, then please upload it together with your resubmission.

Response:

Thank you, this is not relevant.

Reviewer’s Comment

Reviewer #1

Comments to the Author

The paper makes a valuable contribution to understanding the performance of CMIP6 climate models in projecting climate scenarios in the specific geographic location (Upper Blue Nile Basin, Northwestern Ethiopia), addressing an urgent and important topic. The paper provides a detailed study about the impacts of climate change in Ethiopia identifying the vulnerabilities of farmers. Some little consideration on the text:

Response

Thank you for pointing this out.

• line 130 "The Pearson correlation...." could be better to put in bold like the other methods .described

Response

We would like to thank you for the comment. We have made revisions accordingly based on the comments. This point has now been made clearer in line 142.

• line 166 there are different text size, is intentional?

Response

Thank you for the comments. We have revised based on the comments and we make the fonts with similar sizes in line 166.

• from line 238 to line 243 there are some repetitions, i.e "....values of r=0.91, r = 0.91, r=0.91 and R2 =0.83, R^2 = 0.83, R2=0.83....", the same in the lines between 252 and 255., and also in the lines between 266 and 269 with different text size.

Response

We would like to thank you for the comments. We have made changes and removed the repetitions from line 238 to 243 and 252 to 255. In addition, we have corrected the fonts with similar sizes and repetitions from line 266 to 269.

• authors should check the numbering of figures and tables, e.g. line 284 "...(Sub.1)..." this is Figure S1, the same in line 294. In line 333 the figure is not 6 but 5, in line 426 Sub 2 is Table S1,........

Response

We would like to thank you for your comments and we have adjusted the figures and tables based on the comments accordingly.

• The conclusion succinctly summarizes the study's main findings may be perhaps it might be useful to add some advice about adaptation measures, with specific examples of such practices relevant to local agricultural practices or governance frameworks.

Response

We greatly appreciate your comments. We have added some recommendations with adaptation options to tackle the effects of global warming like Stakeholders and farming communities should engage in various adaptation practices such as afforestation and reforestation campaigns and resource conservation mechanisms to address the impacts of warming temperatures. As suggested, we have added these advice about adaptation measures to the conclusion, which reads as: “These significant changes in climate variables contribute to alter the occurrences and se-verity of extreme events. Hence, stakeholders and farming communities should adopt various adaptation practices such as afforestation and reforestation campaigns and resource conservation mechanisms to address the impacts of warming temperatures.”

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript is written well. However, there are some points in the manuscript that needs to be incorporated to further improvement. The paper needs the below-mentioned editing and revision before it is considered for publication in the Journal.

1.      Please revise the title; make it simplified and concise.

2.      The introduction section needs to be revised and improved, as it does not adequately reflect the study's motivation, the applicability of the methods, the objectives, or the significance of the research.

3.      What are the logic behind dividing and selection of these particular the studied period: (2015-2044), mid (2045-2074), and far (2075-2100) time-scales. Please clarify it.

4.      Did you use a common resolution by applying any downscaling or interpolation methods? What was the target/common resolution after downscaling for both the model and observed data? How reliable is the climate information from these GCMs when interpolated to a common resolution?

5.      In Section 3.1, the author mentions that model performance was examined after bias correction. However, I believe this is not a reasonable or reliable way to evaluate the model. Model evaluation should primarily be conducted using the raw model, without bias correction. Afterward, you can apply bias correction and compare the model's performance in both the bias-corrected and pre-bias-corrected conditions.

6.      Model evaluation for model historical period must be evaluated against observed data ensuring same resolution and it should be better to consider both spatial and temporal aspects.

7.      I would suggest to consider seasonal cycle for model evaluation.

8.      In Figure 7, you compare the observed distribution with the model-simulated future distribution. This approach is not reliable, as comparing the spatial distribution of observed data with future projections is not appropriate. Only the model-simulated historical period should be compared against observed data. I suggest using relative changes, such as comparing 'model-historical with model-future' and 'observed with model-future.' This way, you can draw conclusions on how these relative changes vary or match.

9.      Please add the specific time period considered for each Figure and Table caption.

 

Comments on the Quality of English Language

Overall fine.

Author Response

Response to reviewers’ comments (climate-3172478)

We would like to thank the editor and the reviewers for their constructive and valuable comments, and for providing us an opportunity to revise our manuscript. We also appreciate your efforts and time to improve the manuscript. We have tried to address all the comments, questions and made the necessary changes to improve our revised manuscript. Our detailed responses to these comments are provided below.

We have numbered all the comments for ease of cross-referencing. The blue-coloured text in the revised manuscript shows the changes done in track change mode to our original submission. The line numbers refer to the track changed revised manuscript. The italic text in responses below shows the new text added/edited in response to the reviewers’ comments in the revised manuscript. 

Editor’s comments

1. Please download the latest version of the manuscript for revision. Your original submission may have been changed.

Response:

We thank the editor for providing us an opportunity to respond to the reviewers’ comments and revise our manuscript accordingly. As suggested, we have downloaded and carefully addressed all the reviewers' comments that have helped us to substantially improve the quality of our manuscript. 

2. If there has been a change in the authorship during revisions of your paper, please download the "Authorship Change Form" to provide details of the change, then please upload it together with your resubmission.

Response:

Thank you, this is not relevant.

Reviewer #2

Comments to the Author

The manuscript is written well. However, there are some points in the manuscript that needs to be incorporated to further improvement. The paper needs the below-mentioned editing and revision before it is considered for publication in the Journal.

Response

Thank you for your effort to improve the quality of the paper.

• Please revise the title; make it simplified and concise.

Response

Thank you for your insightful comments. As suggested, we have now simplified and condensed the title to read: “Performance evaluation of CMIP6 climate model projections for precipitation and temperature in the Upper Blue Nile Basin, Ethiopia.”

• The introduction section needs to be revised and improved, as it does not adequately reflect the study's motivation, the applicability of the methods, the objectives, or the significance of the research.

Response

We would like to thank you for your comments. We have revised the introduction in lines 65–77 and 84–89 to read as follows: “Coupled Model Intercomparison Project phase six (CMIP6) includes improvements to current parameterizations, the inclusion of recently developed physical processes, and in-creased resolution in compared with other CMIP generations [16]. In addition, CMIP6 global model was set up to assess the effectiveness of GCMs in simulating past, present, and future climate variables under various conditions. Recently, the CMIP6 has been re-leased which integrated the representative concentration pathways (RCPs) and shared so-cioeconomic pathways (SSPs) (population, technology, gross domestic product (GDP), and land-use scenarios) and made projections more authentic [16]. More importantly, bias correction is a statistical approach used to address data exhibiting systematic errors or biases, thereby adjusting it to its correct values [29]. It corrects for the propensity to un-derestimate/overestimate the mean value of downscaled variables by using the statistics of observed and historically simulated variables for similar periods [30]. As a result, out-puts from GCMs often cannot be used directly for climate assessments [31]. Bias correction methods include linear scaling (LS), power transformation (PT) (for rainfall), local intensity scaling, distribution mapping (DM), variance scaling (for temperature), delta change, and quantile mapping (QM) [32,33].

There have been studies conducted in the study area evaluating the quality of CMIP6 climate model outputs using GCM simulations. Therefore, this study addressed the issues by evaluating the performance of the CMIP6 climate model and projecting precipitation and temperature variables under multiple scenarios in different agro-ecological zones of UBNB. This will enable to formulate effective strategies for mitigation as well as adaptations for climate-related impacts. In addition, projection as well as identification of past and future changes in climate extremes is important to analyse heatwave duration, heat related illness, fire risk, livestock heat stress, agricultural productivity decline, and so on. Similarly, the study is critical for disaster-prone locations in order to design adaptation and mitigation actions. This is especially relevant when utilizing the latest released global climate models (GCM) from CMIP Phase 6.”

• What are the logic behind dividing and selection of these particular the studied period: (2015-2044), mid (2045-2074), and far (2075-2100) time-scales. Please clarify it.

Response

Thank you for your comment. We have added justifications about the division of time periods from line 115 to line 124. Here, we have classify the years in more than 25-year period interval for climate analysis and make it easy to analysis.  “In addition, the division of time periods into near (2015-2044), mid (2045-2074), and far (2075-2100) time-scales is commonly used in climate studies and other long-term projections. For instance, near-term (2015-2044) period shows immediate responses to current levels of greenhouse gases already present in the atmosphere. Changes in temperature and precipitation here are largely influenced by short-term climate variability (like ENSO) and existing human actions. It allows assessing short-term adaptation needs and immediate mitigation efforts. Mid-term (2045-2074) reflects the increasing influence of cumulative emissions and socio-economic development choices made over the preceding decades. It helps evaluate the effectiveness of current mitigation policies and guides medium-term adaptation strategies in sectors like agriculture, water management, and infrastructure. The far-term is critical for assessing high-risk scenarios, long-term impacts on ecosystems, and extreme events such as intensified droughts or floods. Therefore, understanding these differences helps local policymakers decide between adaptation strategies based on different future scenarios. Many climate models, such as those used by the Intergovernmental Panel on Climate Change (IPCC), project future scenarios over several decades.”

These models typically divide the 21st century into multiple time periods to assess the effects of different factors (e.g., greenhouse gas emissions, policy changes) on the climate system. This makes the analysis of 30-year periods well-suited to understanding climate variability and long-term changes. Many climate models (e.g., IPCC assessments) and environmental policies are structured around decades, often considering 30-year periods for consistency in statistical analysis. These time scales allow for clear comparisons across different climate models and scenarios, especially when assessing temperature changes, precipitation patterns, and extreme weather events over the course of the century.

In summary, the division of the studied periods into 2015–2044 (near), 2045–2074 (mid), and 2075–2100 (far) is grounded in the need for manageable, consistent, and practical intervals that align with climate models, socio-economic planning, policy cycles, and infrastructure lifecycles. These divisions also balance the need to assess short-term impacts, mid-term transitions, and long-term outcomes for strategic planning.

• Did you use a common resolution by applying any downscaling or interpolation methods? What was the target/common resolution after downscaling for both the model and observed data? How reliable is the climate information from these GCMs when interpolated to a common resolution?

Response

We appreciate your comments. As we know CMIP6 climate dataset is a global model which consists of several model data’s with different spatial resolutions. Therefore, in this paper, we applied statistical downscaling methods then after we have made correction for the bias of each models to fit our area of interest. We have extract the model data from the global CMIP6 dataset based on our observed/station locations/points. The interpolate it to create continuous features of the study area without overlapping the models one on another.

•  In Section 3.1, the author mentions that model performance was examined after bias correction. However, I believe this is not a reasonable or reliable way to evaluate the model. Model evaluation should primarily be conducted using the raw model, without bias correction. Afterward, you can apply bias correction and compare the model's performance in both the bias-corrected and pre-bias-corrected conditions.

Response

We would like to thank you for your comments and we strongly agreed with the issues you raised. However, the bias corrections were conducted using CMhyd software as we mentioned in 2.3.1.  Hence, the software provides both the raw and bias corrected data then after we evaluate the performance of those models using statistical techniques by comparing raw and corrected data. Therefore, “model performance was examined after bias correction” it does not mean that, the performance evaluation was made using only corrected (after bias correction) data.

• Model evaluation for model historical period must be evaluated against observed data ensuring same resolution and it should be better to consider both spatial and temporal aspects.

Response

Thank for your comments and suggestions. We have considered that both the observed and model historical data have similar time lengths (1995-2014) and resolutions. However, we already extract the model time-series data from CMIP6 GCMs using point stations.

•  I would suggest to consider seasonal cycle for model evaluation.

Response

We would like to thank you for your suggestion. We will go further on the seasonal cycle for evaluation. In addition, we did not see the significance of considering the seasonal cycle for evaluations.

•  In Figure 7, you compare the observed distribution with the model-simulated future distribution. This approach is not reliable, as comparing the spatial distribution of observed data with future projections is not appropriate. Only the model-simulated historical period should be compared against observed data. I suggest using relative changes, such as comparing 'model-historical with model-future' and 'observed with model-future.' This way, you can draw conclusions on how these relative changes vary or match.

Response

Thank you for your comment. We also appreciate the way you see the issues that needs justifications. In figure 7, we tried to show the spatial distributions of each variables (Temperature and rainfall) in historical and future time-periods under each scenarios based on selected models.

•  Please add the specific time period considered for each Figure and Table caption.

Response

We would like to thank you for your comments and we have add the time-periods for each figures and tables based on the comments in lines 250, 272, 293, 335, 363, 380, 426, 476,

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This study evaluated the performance of the CMIP6 climate model in predicting precipitation and temperature variables in the Upper Blue Nile Basin (UBNB) in Northwestern Ethiopia. This manuscript offers valuable insights; however, there are several significant concerns that the authors must address before publication can be considered.

1. Introduction: The introduction is insufficient to provide context for the research. It is recommended to include literature on the latest applications of CMIP6.

2. Methodology: What is the basis for selecting seven CMIP6 GCMs?

3. Line 86: “Map of the study area”, Why is it bolded, and which specific elements should be emphasized?

4. It is recommended to move the section from lines 103 to 112 to the Introduction.

5. Line 239: “R^² = 0.88.” Is this a formatting error?

6. Line 243-244: The values overestimated in the model when the PBIAS value is positive, while it is underestimated when it is negative.” Why is the font size of this section larger than the rest? Is it intended to emphasize the results?

7. The ME, MAE, and RMSE metrics have been defined in Section 2.4, and the abbreviations can be used directly later. Please conduct a thorough text check.

8. Please adjust the layout and fonts of Figure 4, as it currently appears somewhat crowded.

9. Unify the font size for pictures, tables, and formulas.

Author Response

Response to reviewers’ comments (climate-3172478)

We would like to thank the editor and the reviewers for their constructive and valuable comments, and for providing us an opportunity to revise our manuscript. We also appreciate your efforts and time to improve the manuscript. We have tried to address all the comments, questions and made the necessary changes to improve our revised manuscript. Our detailed responses to these comments are provided below.

We have numbered all the comments for ease of cross-referencing. The blue-coloured text in the revised manuscript shows the changes done in track change mode to our original submission. The line numbers refer to the track changed revised manuscript. The italic text in responses below shows the new text added/edited in response to the reviewers’ comments in the revised manuscript. 

Editor’s comments

1. Please download the latest version of the manuscript for revision. Your original submission may have been changed.

Response:

We thank the editor for providing us an opportunity to respond to the reviewers’ comments and revise our manuscript accordingly. As suggested, we have downloaded and carefully addressed all the reviewers' comments that have helped us to substantially improve the quality of our manuscript. 

2. If there has been a change in the authorship during revisions of your paper, please download the "Authorship Change Form" to provide details of the change, then please upload it together with your resubmission.

Response:

Thank you, this is not relevant.

Reviewer #3

Comments to the Author

This study evaluated the performance of the CMIP6 climate model in predicting precipitation and temperature variables in the Upper Blue Nile Basin (UBNB) in Northwestern Ethiopia. This manuscript offers valuable insights; however, there are several significant concerns that the authors must address before publication can be considered.

Response

Thank you for your valuable comments to improve this manuscript.

3.1 Introduction: The introduction is insufficient to provide context for the research. It is recommended to include literature on the latest applications of CMIP6.

Response

We would like to appreciate you for the comments. We have add one paragraph about CMIP6 in lines 65 – 89 that read as: In addition, CMIP6 global model was set up to assess the effectiveness of GCMs in simulating past, present, and future climate variables under various conditions. Recently, the CMIP6 has been re-leased which integrated the representative concentration pathways (RCPs) and shared so-cioeconomic pathways (SSPs) (population, technology, gross domestic product (GDP), and land-use scenarios) and made projections more authentic [16]. More importantly, bias correction is a statistical approach used to address data exhibiting systematic errors or biases, thereby adjusting it to its correct values [29]. It corrects for the propensity to un-derestimate/overestimate the mean value of downscaled variables by using the statistics of observed and historically simulated variables for similar periods [30]. As a result, out-puts from GCMs often cannot be used directly for climate assessments [31]. Bias correction methods include linear scaling (LS), power transformation (PT) (for rainfall), local intensity scaling, distribution mapping (DM), variance scaling (for temperature), delta change, and quantile mapping (QM) [32,33].

There have been studies conducted in the study area evaluating the quality of CMIP6 climate model outputs using GCM simulations. Therefore, this study addressed the issues by evaluating the performance of the CMIP6 climate model and projecting precipitation and temperature variables under multiple scenarios in different agro-ecological zones of UBNB. This will enable to formulate effective strategies for mitigation as well as adaptations for climate-related impacts. In addition, projection as well as identification of past and future changes in climate extremes is important to analyse heatwave duration, heat related illness, fire risk, livestock heat stress, agricultural productivity decline, and so on. Similarly, the study is critical for disaster-prone locations in order to design adaptation and mitigation actions. This is especially relevant when utilizing the latest released global climate models (GCM) from CMIP Phase 6.”

3.2   Methodology: What is the basis for selecting seven CMIP6 GCMs?

Response

Thank you for your question. We have selected seven CMIP6 global models for this paper. Those models were selected based on the following conditions such as:

• Variable (precipitation, maximum and minimum temperature)
• Scenarios (SSP1-2.6 (low-emission scenario), SSP2-4.5 (medium-emission scenario), and SSP5-8.5 (worst-emission scenario)).
• Spatial resolution less than 1⁰*1⁰ and
• Based on different literatures (many studies recommend them).

3.3  Line 86: “Map of the study area”, Why is it bolded, and which specific elements should be emphasized?

Response

We would like to thank you for the comment. We have corrected the comments on line 86. In addition, the map includes meteorological stations and altitudinal variations in the study area.

3.4  It is recommended to move the section from lines 103 to 112 to the Introduction.

Response

Thank you for your recommendation. We have moved the sections from line 103 to 112 to the introduction accordingly the comments.

3.5  Line 239: “R^² = 0.88.” Is this a formatting error?

Response

We would like to appreciate you for the questions and comments. We have corrected the comments accordingly.

3.6 Line 243-244: The values overestimated in the model when the PBIAS value is positive, while it is underestimated when it is negative.” Why is the font size of this section larger than the rest? Is it intended to emphasize the results?

Response

Thank you for the comments. We have made corrections accordingly. It is not to emphasize the results rather it is an error and we have made corrections.

3.7 The ME, MAE, and RMSE metrics have been defined in Section 2.4, and the abbreviations can be used directly later. Please conduct a thorough text check.

Response

We would like to say thank you for the comments. Therefore, the comments have addressed as follows on line 229 to 232: The statistical methods used to evaluate the performances were including the Pearson correlation coefficient (r), mean error (ME), The root mean square error (RMSE), and percent of bias (PBias).

3.8  Please adjust the layout and fonts of Figure 4, as it currently appears somewhat crowded.

Response

Thank you for the comment. We have made adjustments based the comments on Figure 4. However, we have faced challenges that if we made changes, the fonts are not clearly visible.

3.9  Unify the font size for pictures, tables, and formulas.

Response

Thank you for the comments. We have made corrections accordingly.

 

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

General comments:

The manuscript evaluates several GSMs for the Upper Blue Nile Basin region, Northwestern Ethiopia. Data from different GCMs for precipitation and air temperature are compared with data from Ethiopian weather stations. On this basis, two models have been selected that have the best performance in terms of the two climatic elements. After certain corrections, these two GCMs are used to project precipitation and maximum and minimum air temperatures until the end of the current century. The topic is important in relation to the necessary adaptation measures to the expected climate changes. However, the manuscript has some significant weaknesses that need to be addressed. They are primarily related to the selection of GCMs and their validation.

The aim of the study should be more clearly stated in the Introduction section.

In section 2.2 and in conjunction with Table 1, more information should be included as to why only these 7 GCMs were selected. The usual practice is to use and assess all available models. An ensemble of models should also be considered in such type of study.

The climatic zones used in the study should be defined in the Data and Methods section.

In section 3.1 and in relation to the cited reference 12 (l.232) some further discussion needs to be included. Reference 12 uses a different methodology to validate GCMs. It compares data from different weather stations with different reanalyses and selects the best reanalysis, which is then used to validate the outputs of the GCMs. In the present study, data from different weather stations are compared directly with those from GCMs. The data from the weather stations, however, are valid for a certain point in contrast to the data of reanalyses and GCMs, which cover the entire territory or water area. Which of the two methodologies is more appropriate for this type of research?

Specific comments:

l.149 Mi is model estimate rather than satellite estimate?

l.178 the statistic SSs should be explained.

l.189 formula 8 - should both (upper and lower) S be less than zero?

l.196 and 199 – Ti or mi?

l.238-243, l.253-255, l.267-269 – why same values are repeated three times?

Table 4 has major issue – R² values could not be negative. Please correct.

l.321-322 the captions of Figure 4, Figure 5 and Figure 6 should include also the GCM, which was used for calculations.

l. 333 – Figure 6 should be Figure 5.

Figure 7 should be corrected. Projected precipitation and air temperatures under different scenarios should be shown as a difference from the baseline period.

l.465 Table 6 should be Table 5.

Comments on the Quality of English Language

The English language needs some improvement.

Author Response

Response to reviewers’ comments (climate-3172478)

We would like to thank the editor and the reviewers for their constructive and valuable comments, and for providing us an opportunity to revise our manuscript. We also appreciate your efforts and time to improve the manuscript. We have tried to address all the comments, questions and made the necessary changes to improve our revised manuscript. Our detailed responses to these comments are provided below.

We have numbered all the comments for ease of cross-referencing. The blue-coloured text in the revised manuscript shows the changes done in track change mode to our original submission. The line numbers refer to the track changed revised manuscript. The italic text in responses below shows the new text added/edited in response to the reviewers’ comments in the revised manuscript. 

Editor’s comments

1. Please download the latest version of the manuscript for revision. Your original submission may have been changed.

Response:

We thank the editor for providing us an opportunity to respond to the reviewers’ comments and revise our manuscript accordingly. As suggested, we have downloaded and carefully addressed all the reviewers' comments that have helped us to substantially improve the quality of our manuscript. 

2. If there has been a change in the authorship during revisions of your paper, please download the "Authorship Change Form" to provide details of the change, then please upload it together with your resubmission.

Response:

Thank you, this is not relevant.

Reviewer #4

Comments to the Author

General comments: The manuscript evaluates several GSMs for the Upper Blue Nile Basin region, Northwestern Ethiopia. Data from different GCMs for precipitation and air temperature are compared with data from Ethiopian weather stations. On this basis, two models have been selected that have the best performance in terms of the two climatic elements. After certain corrections, these two GCMs are used to project precipitation and maximum and minimum air temperatures until the end of the current century. The topic is important in relation to the necessary adaptation measures to the expected climate changes. However, the manuscript has some significant weaknesses that need to be addressed. They are primarily related to the selection of GCMs and their validation.

Response

Thank you for your valuable comments to improve this manuscript.

4.1 General comments: The aim of the study should be more clearly stated in the Introduction section.

Response

We would like to thank you for your comments, questions, and suggestions. We have added the aim of the study in lines 83-88 that read as: “. In addition, projection as well as identification of past and future changes in climate extremes is important to analyse heatwave duration, heat related illness, fire risk, livestock heat stress, agricultural productivity decline, and so on. Similarly, the study is critical for disaster-prone locations in order to design adaptation and mitigation actions. This is especially relevant when utilizing the latest released global climate models (GCM) from CMIP Phase 6.”  

4.2  General comments:  In section 2.2 and in conjunction with Table 1, more information should be included as to why only these 7 GCMs were selected. The usual practice is to use and assess all available models. An ensemble of models should also be considered in such type of study.

Response

Thank you for your comments. We have selected seven CMIP6 global models for this paper. Those models were selected based on the following conditions such as:

• Variable (precipitation, maximum and minimum temperature)
• Scenarios (SSP1-2.6 (low-emission scenario), SSP2-4.5 (medium-emission scenario), and SSP5-8.5 (worst-emission scenario)).
• Spatial resolution less than 1⁰*1⁰ and
• Based on different literatures (many studies recommend them).

4.3  General comments: The climatic zones used in the study should be defined in the Data and Methods section.

Response

Thank you for the comments. We have added the climatic zone classification of the study area from line 101 to 105  that read as: “In addition, the study area is endowed with five agro-ecological zones based on its altitudinal classification, namely, desert from the lowest altitude (478 masl to 500 masl), lowland (Kolla) from (500 masl to 1500 masl, midland (Woina-dega) 1500 to 2300 masl, highland (Dega) 2300 to 3200 masl and upper highland/cold (Wurch) 3200 to 4260 masl.”

4.4  General comments:  In section 3.1 and in relation to the cited reference 12 (l.232) some further discussion needs to be included. Reference 12 uses a different methodology to validate GCMs. It compares data from different weather stations with different reanalyzes and selects the best reanalysis, which is then used to validate the outputs of the GCMs. In the present study, data from different weather stations are compared directly with those from GCMs. The data from the weather stations, however, are valid for a certain point in contrast to the data of reanalyzes and GCMs, which cover the entire territory or water area. Which of the two methodologies is more appropriate for this type of research?

Response

Thank you for the comments. In this paper, we evaluated the performance of seven CMIP6 climate models with observed/meteorological station data from 1995-2014. Reference 12 were evaluated Model data with the reanalysis dataset after comparing with area-averaged ground observational data. They first tried to evaluate the performance of reanalysis data against the observed dataset. In addition, as we have seen, evaluate the models against the observed data is more appropriate than as evaluated against the reanalysis dataset.

4.5 Specific comments: l.149 Mi is model estimate rather than satellite estimate?

Response

We would like to thank the specific comments. We have made corrections as model estimates.

4.6 Specific comments: l.178 the statistic SSs should be explained.

Response

we corrected "SSs" to "S," which stands for statistic. This refers to a test that analyses the sign of the difference between later-measured data and earlier-measured data. Each later-measured value is compared to all values measured earlier. When S is a large positive number, it indicates that later-measured values tend to be larger than earlier ones, suggesting an upward trend. Conversely, when S is a large negative number, it suggests that later values tend to be smaller, indicating a downward trend. If the absolute value of S is small, no trend is indicated. In the manuscript the S is explained from line 184 – 217.

4.7 Specific comments: l.189 formula 8 - should both (upper and lower) S be less than zero?

Response

We would like to thank for the questions. We have made corrections accordingly.

4.8 Specific comments: l.196 and 199 – Ti or mi?

Response

Thank you for the comments. We have made corrections accordingly. The slope (mi).

4.9 Specific comments: l.238-243, l.253-255, l.267-269 – why same values are repeated three times?

Response

We would like to thank the comments. We have made corrections accordingly.

4.10 Specific comments: Table 4 has major issue – R2 values could not be negative. Please correct.

Response

We thank you for the comments. We have made corrections based on the comments.

4.11 Specific comments: l.321-322 the captions of Figure 4, Figure 5 and Figure 6 should include also the GCM, which was used for calculations.

Response

Thank you for your comments. We have added the GCMs to the supplementary materials for clarity and ease of understanding. Additionally, the models are listed in Table 2, Table 3, and Table 4. We selected the best models based on their performance: INM-CM5-0 for precipitation and INM-CM4-8 for temperature. We have also removed the GCMs from the captions of Figures 4, 5, and 6 for better clarity

4.12 Specific comments: l. 333 – Figure 6 should be Figure 5.

Response

Thank for the comment. We have made corrections accordingly.

4.13 Specific comments: Figure 7 should be corrected. Projected precipitation and air temperatures under different scenarios should be shown as a difference from the baseline period.

Response

Thank you for your comment. We also appreciate the way you see the issues that need justifications. In Figure 7, we tried to show the spatial distributions of each variable (Temperature and rainfall) in historical and future time periods under each scenario based on selected models. For ease of understanding and to feel the spatial differences in baseline and future time periods, we have mapped it together.

4.14 Specific comments: l.465 Table 6 should be Table 5.

Response

Thank for the comment. We have made corrections accordingly.

4.15 The English language needs some improvement.

Response

Thank you for the comment. We have made the necessary language corrections throughout the manuscript. For instance, revisions were made in lines 14–32, 39–40, 58–69, 78–82, 95–96, 113–120, 137–141, 479–484, 502–507, 537–538, 574–586, and 600–606.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The authors satisfactorily revised the manuscript considering all of my suggested comments which certainly improved the quality of the paper. I would suggest to accept this paper to publish in the journal.

Comments on the Quality of English Language

It's fine

Author Response

Thank you for your positive feedback and recommendation to accept our paper for publication. We greatly appreciate your insightful comments and suggestions, which have contributed to improving the quality of our manuscript. In addition, as requested, we have carefully reviewed and edited the manuscript to improve the quality of the English language. We hope these enhancements meet your expectations.

We are delighted that the revisions have met your expectations and are grateful for your support throughout the review process.

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscirpt have been rivised according at suggestion. So the acception is suggested.

Author Response

Thank you for your feedback and for recommending the acceptance of our manuscript. We appreciate your valuable input during the review process and are pleased that the revisions have addressed your suggestions.

Your contributions have been instrumental in improving the quality of our work, and we are grateful for your time and effort.

Reviewer 4 Report

Comments and Suggestions for Authors

It is still unclear if all the negative numbers in Table 4 are corrected in the revised manuscript.

The addition of years at the end of the captions of Figures 4, 5 and 6 is not necessary because they are present in the middle of the captions.

Comments on the Quality of English Language

Minor editing is still needed.

Author Response

Minor editing is still needed. It is still unclear if all the negative numbers in Table 4 are corrected in the revised manuscript. The addition of years at the end of the captions of Figures 4, 5 and 6 is unnecessary because they are present in the middle of the captions.

Thank you for your feedback and for pointing out the remaining issues.

1. We have thoroughly reviewed Table 4 and ensured all the negative numbers are correct in the revised manuscript. Any discrepancies have been rectified.

2. Regarding Figures 4, 5, and 6, we have removed the unnecessary addition of years at the end of the captions, as they are indeed already present in the middle of the captions.

Additionally, as requested, we have made further modifications to improve the clarity and quality of the English language throughout the manuscript.

We appreciate your careful review and constructive comments, which have helped enhance the quality of our paper.