| 02 Mar 2026
Changes in rainfall extremes over Southern Africa over the 20th and 21st centuries simulated by a high-resolution regional climate model and their connection to the Agulhas Current System
Abstract. In Southern Africa, precipitation is a crucial variable that is closely linked to agriculture and water supply. Additionally, extreme precipitation leads to devastating flooding, and heavy rainfall events pose a significant threat to the population in this region. Here, we analyse historical and scenario-based climate simulations, focusing on the spatial patterns of extreme precipitation and its projected future changes. We also investigate whether the Agulhas Current, a major regional oceanic current system, influences the frequency or intensity of extreme precipitation.
For this purpose, we analyse high-resolution simulations with the regional atmospheric model CCLM, conducted with a higher resolution (16 km x 16 km) than that normally used in the CORDEX project. One simulation is driven by meteorological reanalysis, whereas other simulations are driven by global coupled simulations that regionally resolve the Agulhas Current, its leakage and retroflection. The simulations cover the last few decades and the 21st century.
During the present period, the regional simulations indicate the strongest precipitation over Madagascar, the Mozambique Channel, and the adjacent mainland. Extreme rainfall events are most intense in Madagascar's mountainous regions, the Drakensberg, and the African Great Lakes. The extremes are generally stronger in the Summer Rainfall Zone than in the Winter Rainfall Zone. This climatological pattern agrees with available observations.
In the scenario simulation, extreme events are projected to intensify along the South African coast. In KwaZulu-Natal province, the heaviest future rainfall event is twice as strong as the strongest extreme simulated in the historical period and the recently observed disastrous extreme event in April 2022.
The simulations do not reveal a discernible impact of variations in the Agulhas Current System on strong rainfall events along the South African coast.
The work of Tim et al uses high resolution climate model simulations to investigate past and future change in extreme rainfall over southern Africa, while also exploring the influence of the Agulhas Current on the coastal regional precipitation. The authors find that the model replicate the observed extreme rainfall over the region and that extreme rainfall is projected to increase. The research presented in this paper aims to contribute to our understanding of extreme rainfall in the region and is of interest for the community. Overall, the paper is well written; however, some restructuring would improve the narrative flow. Additionally, the data used are not well suited for case study analysis, and the methodology requires further improvements and clarification. I also recommend strengthening the conclusions. Below are some suggestions and recommendations:
L37: I’m unclear on what the authors intend by the phrase 'models are burdened…”
L39: The intent of figure 1 is not clear in the introduction and how it fits within the general background.
L42-46: The sentences are not clear, and more context is needed, such as time periods, scenarios and seasons.
L83: Suggest replacing “negative trends’ by “downward trends’
L85: This manuscript (https://doi.org/10.1029/2020EA001466) could be interesting here.
L106-108: I recommend having a look at this paper https://doi.org/10.1002/wcc.70025
L146-149: I recommend adding a box of these regions with labels in one of the figures.
L150: Is this value representing the 99th percentile of wet days?
L51-153: Could you clarify or provide justification for why the order of operations varies depending on the size of the regions?
L163: For the GEV fit, is it assumed that the time series is stationary or a scale fit is included as covariate (e.g. section 4.3.2 https://ascmo.copernicus.org/articles/6/177/2020/)
L170: The authors highlight challenges related to observational data for model evaluation in lines 84-86. Are ERA5, TAMSAT, and CHIRPS the most suitable datasets for this purpose? Could alternatives such as ERA5-Land or Multi-Source Weather (MSWX) offer improvements? For reference, Tim et al. (2023; doi:10.5194/wcd-4-381-2023) utilized different datasets in their analysis.
L174: Would incorporating the seasonal rainfall cycle for the regions identified in Section 3.2 provide additional value here (and section 4.2)? Additionally, I would assume that section 4.1 would be already covered in Tim et al. 2023.
L196: “This study…” It is not clear to me which study is referenced here.
L205: Please clarify what you do with the resolution mismatch of the datasets. This should be clarified in the methods section.
L169: Should section 4 be under Results section?
L206: Figure4. Should the authors also include the regions identified in Section 3.2?
L209: “In our coastal domain…” Please provide boxes identifying the regions in the map.
L216-221: In here the authors should not compare return periods of two datasets with different lengths and periods! Furthermore, it is unclear whether this analysis and its findings contribute meaningful insights.
L222: Is this because of the short sample?
L229-230: I suggest adding this information in the methods section and the reasons. Which Niño index was used and from which dataset was it calculated, what are the regions with high correlation between El Nino and rainfall (see for e.g. 10.1088/1748-9326/ade60e)? As it stands, the analysis appears arbitrary and lacks context within the current narrative. Additionally, I suggest including a paragraph with a literature review on the relationship between El Niño and extreme precipitation to provide necessary context in the introduction.
L237: I recommend merging Sections 4 and 5 to create a more cohesive and accessible narrative. In its current form, the structure is somewhat disjointed and challenging for readers to follow. Additionally, I recommend including a detailed description of the analysis and methods in the methodology section to ensure the study is easily replicable.
L242-247: I am not sure how relevant it is to provide dates of extreme rainfall from climate models that have been run in a climatological setup as the model is not initialized to reproduce the real-world sequence of weather.
L247-248: “Thus, the heaviest rainfall occurs over the Mozambique Channel…”. Mozambique channel is over the ocean and the figure are not showing it!
L250: Would this information be better presented as timeseries for the regions of interest introduced in section 3.2? The current map is ‘too noisy’ and I’m not sure what is the main take away of this information.
L284: Why were this region chosen? It is unclear whether this region experiences the greatest impact from the Agulhas Current.
L293: I’m surprised by the fact that tropical cyclones contribute to extreme rainfall in this region! Are there any references supporting this?
L295: I suggest including these details in the methods. As mentioned before, the analysis appears arbitrary and lacks context within the current narrative.
L296-299: I suggest aligning the definition of tropical cyclones and cut-off-lows with the literature. Cut-off-low do also form north of 30°S as it is the case of Durban floods in April 2022 and others.
L330-389: I am not sure what is the validity of studying specific events with a climate model as they’re not designed to reproduce exact individual historical events exactly as they happened and they have internal variability.
L391-392: This is not the correct.
L392-397: Sentence is unclear.
L400-402: I’m not sure how is this information relevant here. Should this be in the introduction. Furthermore, “Tropical cyclones are projected to intensify” is not clear what aspect of tropical cyclones are projected to intensify and for which ocean basin. And the correct reference should be Seneviratne et al 2021 (https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter11.pdf)
L390: The conclusion needs to be revised considering the above recommendations and suggestions with clearer acknowledgment that the results are based on a single model and simulation, and with the associated uncertainties and limitations explicitly discussed and contextualized (see for e.g. L48-L51). Additionally, do these results improve or are they better/worse than the ones from CORDEX models? What can we learn from these higher resolution simulations for the region that we didn't know before?
And lastly, I suggest improving the colours in the maps. Here is a visual guide that may be helpful
https://www.ipcc.ch/site/assets/uploads/2022/09/IPCC_AR6_WGI_VisualStyleGuide_2022.pdf