Projected Temperature and Precipitation Changes in Brazzaville, Republic of Congo: A CMIP6-Based Analysis

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Despite advances in global climate modelling (GCM), the lack of high-resolution and regional-specific projections limits data-driven adaptation strategies, hindering climate resilience planning. This study aims to assess future temperature and precipitation changes in Brazzaville using CMIP6 climate projections across different time scales: historical time (1981–2010), mid-century time (2031–2060), and late-century time (2071–2100). The study employs climate modelling and statistical analysis, utilizing CMIP6 projections from the ACCESS-CM2 model under the SSP2-4.5 scenario. Climate data were processed using MATLAB, incorporating bias correction (quantile mapping), statistical downscaling (CORDEX methods), trend analysis, and correlation assessments. Key climate indicators, including temperature and precipitation, were evaluated using mean, median, and standard deviation. Results show a statistically significant warming trend, with mean temperatures projected to increase by 3.5°C (± 0.2°C) between 2071 and 2100 compared to the historical baseline. Monthly mean temperatures may exceed 32.1°C during peak heat events, with seasonal anomalies suggesting an increase in extreme heat events beyond the 95th percentile. Precipitation projections show a + 6.8% increase in peak wet season rainfall, while dry season precipitation is projected to decline by − 10.3%, worsening seasonal contrasts. The probability of extreme precipitation events (> 90 mm/day) increases by 14.7%, showing a higher risk of flooding and runoff intensification. Meanwhile, consecutive dry days (CDDs) in the dry season are expected to increase by + 8 days per year, heightening drought severity and soil moisture deficits. Future microclimate is expected to undergo significant variability due to global climate change. Climate projections SSP scenarios Temperature variability Precipitation trends Brazzaville Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Highlights • The research uses CMIP6 climate projections under the SSP2-4.5 scenario. • It focuses on future climate change impacts on temperature and precipitation in Brazzaville. • Climate change impacts are analysed across three-time horizons: historical (1981–2010), mid-century (2031–2060), and late-century (2071–2100). • The study applies statistical downscaling, bias correction, and trend analysis using observed climate data for model calibration. I. Introduction Climate change refers to variations in climate variables, particularly temperature and precipitation [ 1 , 2 ]. The global average temperature has increased by approximately 0.74°C between 1906 and 2005 [ 3 ], with two major warming phases: from 1910 to the 1940s and more significantly from the 1970s to 2005. Rising temperatures lead to increased evapotranspiration and alter global precipitation patterns [ 4 , 5 , 6 ]. These changes influence temperature and precipitation trends, affecting the frequency and intensity of extreme events such as floods and droughts at regional and local scales [ 7 , 8 , 9 ]. The high concentration of greenhouse gases in the atmosphere contributes to rising temperatures and changes in rainfall distribution and intensity [ 6 , 10 ]. These changes affect monthly and seasonal trends, and the interannual variability of climatic conditions. The beginning of the 21st century is recorded as the warmest period [ 6 ], and climate change impacts are expected to persist with continued increases in temperature and rainfall variability [ 11 , 12 ]. Africa is particularly vulnerable to climate change due to its heavy dependence on climate-sensitive sectors such as agriculture [ 1 , 2 ]. The continent is among the most affected regions [ 3 , 6 ]. Future climate projections for Central Africa indicate increasing temperatures and shifts in precipitation patterns. Similarly, future climate projections show significant changes for the Republic of Congo, particularly in the Brazzaville region. Bouka-Biona & Mpounza in 2009 and Cyriaque Mbingui in 2022 suggest that by the mid-to-late 21st century, climate change could lead to increased temperatures and variability in rainfall patterns in the Republic of Congo, posing challenges to water resource management, agriculture, and urban infrastructure. IPCC reports showed the presence of climate change at the international level based on different scenarios. The current report of the IPCC, which is used for different impact assessment studies, is based on the Shared Socioeconomic Pathways (SSPs) scenarios. It is the latest generation that provides input to climate models, which integrate different socioeconomic development trajectories with greenhouse gas emissions scenarios. These scenarios provide a more comprehensive framework for assessing climate change risks. Five main scenarios have been defined with different socioeconomic pathways. The SSP1-1.9: Assumes very low greenhouse gas emissions, with CO₂ emissions reaching net zero around 2050. Projected global warming is approximately 1.4°C by 2081–2100. SSP1-2.6: Assumes low greenhouse gas emissions, with CO₂ emissions reaching net zero around 2075. Projected global warming is approximately 1.8°C by 2081–2100. SSP2-4.5: Assumes intermediate greenhouse gas emissions, with CO₂ emissions remaining around current levels until 2050, then declining but not reaching net zero by 2100. Projected global warming is approximately 2.7°C by 2081–2100. SSP3-7.0: Assumes high greenhouse gas emissions, with CO₂ emissions doubling by 2100. Projected global warming is approximately 3.6°C by 2081–2100. SSP5-8.5: Assumes very high greenhouse gas emissions, with CO₂ emissions tripling by 2075. Projected global warming is approximately 4.4°C by 2081–2100. Climate change impacts are spatially and temporally variable [ 13 ], making localized studies essential for developing effective adaptation strategies. While global climate models (GCMs) have been extensively used, newer climate projections based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) provide improved regional climate assessments. This study aims to analyse climate projections for the region of Brazzaville using CMIP6 models, focusing on temperature and precipitation trends for three periods: 1981–2010 (historical baseline), 2031–2060 (mid-century), and 2071–2100 (end of the century). The results will help inform policies and strategies for climate adaptation and mitigation. The primary objective of this study is to estimate the potential impact of climate change on future temperature and precipitation patterns in the Brazzaville region. The specific objectives include: (1) Assessing historical climate variability in the region. (2) Analysing projected changes in temperature and precipitation based on the SSP2-4.5 scenario. And (3) Evaluating potential implications for ecosystems, community, and livelihood. By providing a robust scientific foundation for climate adaptation strategies, this study aims to support sustainable development efforts in the Republic of Congo. Understanding future climate scenarios will enable policymakers and stakeholders to implement effective measures and reduce vulnerability to climate-related risks. II. Materials and methods 1. Area of study The Brazzaville region is the capital city of the Republic of Congo, located in the south of the country, on the right bank of the Congo River. With a population estimated at around 34.9% of the country's 6,142,180 inhabitants [16], the city spans over 264 km² out of the 342,000 km² of the country [17]. The region is geographically located at -4°7"48" and -4°20'24" south latitude, and 15°7'12" and 15°19'48" east longitude. According to the Köppen-Geiger classification, the microclimate reflects a humid tropical climate [19], characterized by seasonal variability with two distinct seasons, a dry season from June to September and a rainy season from October to May, with two wetter periods between March-April and November-December [17,19]. During the dry season, average temperatures vary between 18°C and 38°C, while during the rainy season they fluctuate between 22°C and 33°C [19,21]. Rainfall totals 1,095 millimetres per year, making it abundant [19]. Soils are generally ferralitic and hydromorphic, poor in nutrients and organic matter. They are affected by a steady decline in fertility, increased acidification, and susceptibility to water and wind erosion, mainly due to land use and urbanization. The region often experiences erosion, flooding, and other events without well-documented empirical evidence of climate change. 2. Materials and methods 2.1 Research design This study adopts a mixed-methods approach, integrating both quantitative and qualitative methods to analyse future climate projections in Brazzaville. The research combines climate modelling and statistical analysis to assess trends in temperature and precipitation over different periods. key periods include historical baseline (1981–2010), mid-century or future One period (2031–2060), and late-century or future Two period (2071–2100). By employing statistical metrics (mean, median, standard deviation, and trend analysis) and contextual insights from the literature review, the study ensures a comprehensive understanding of climate variability and its probable implications. 2.2 Climate Variables and Metrics Key climate indicators analysed included temperature and precipitation, both assessed using mean, median, and standard deviation. Trend analysis was conducted across monthly, seasonal, and interannual timescales to identify patterns and long-term changes. Additionally, correlation analysis was performed to examine the relationship between temperature and precipitation variations. Data processing and visualization were performed to enable statistical analysis, trend detection, and spatial representation. Historical climate data for the period 1981–2010 were sourced from Climate Explorer, while future projections for 2031–2060 and 2071–2100 were extracted from CMIP6 simulations. 2.3 Data analysis The data analysis was conducted using MATLAB, which helped with statistical computations, trend analysis, correlation assessments, and graphical visualizations of climate variables. Climate projections were obtained from CMIP6 (Coupled Model Intercomparison Project Phase 6), using the ACCESS-CM2 model. These models were selected based on their high spatial resolution and ability to simulate regional climate processes accurately. The study adopted the SSP2-4.5 (Shared Socioeconomic Pathway 2-4.5) scenario, which represents a moderate emissions trajectory suitable for the economic and environmental context of the Republic of Congo. The data analysis followed a systematic processing approach, beginning with data preprocessing, where raw climate datasets were cleaned, formatted, and structured. Missing data were handled using statistical interpolation techniques, and spatial and temporal consistency was ensured. Bias correction was applied to CMIP6 model outputs to minimize systematic errors, following CORDEX approaches. After preprocessing, trend analysis was performed to assess long-term changes in temperature and precipitation across monthly, seasonal, and interannual timescales. Time-series decomposition techniques were used to separate long-term trends, seasonal cycles, and residual variability. To evaluate climate variability, Pearson correlation analysis and linear regression models were applied to examine relationships between temperature and precipitation over different time frames. Then, comparative trend graphs were generated to highlight differences between historical and future climate conditions. III. Results and discussion The analysis of future climate projections reveals significant temperature and precipitation pattern shifts, with implications for long-term climate variability and extreme weather events. By examining historical trends (1981–2010) alongside mid-century (2031–2060) and late-century (2071–2100) projections, the results highlight a progressive increase in temperature, modifications in seasonal rainfall distribution, and growing interannual variability. 1. Precipitation trends 1.1 Variation in precipitation over the three periods in terms of mean, median, and standard deviation Figure 3 shows changes in the relative deviation of monthly cumulative precipitation compared to the historical period for the three scenarios over the 21st century, with distinct trends in monthly means, medians, and standard deviations. It shows increased variability with rainfall trends unevenly distributed over the three periods. The historical data (1981–2010) exhibit a distinct bimodal rainfall pattern, characteristic of the region’s equatorial climate. Peak precipitation occurs between March-May and October-December, while a pronounced dry season is observed from June to August. The interquartile range (IQR) of rainfall distribution shows substantial variability, particularly during the peak rainfall months of April, October, and November. The presence of outliers further indicates instances of extreme precipitation events, highlighting the inherent fluctuations in the historical climate. Mid-century projections (2031–2060) indicate a shift in rainfall distribution, with notable changes in seasonal patterns. The median rainfall values during the dry season (June-August) appear lower, suggesting a potential intensification of drought conditions. Conversely, rainfall variability during the wettest months increases, as evidenced by the widened IQR and the frequency of outliers. This suggests an increased likelihood of extreme weather events, including heavy rainfall episodes interspersed with drier periods, which could have significant implications for water resource management, agriculture, and disaster preparedness (High confidence). By the late century (2071–2100), the projections reveal a more pronounced alteration in rainfall patterns. The wet season experiences higher median precipitation levels, indicating intensified rainfall during peak months. The extended whiskers and increased number of outliers suggest heightened interannual fluctuations, implying greater uncertainty in seasonal rainfall distribution. The dry season remains markedly arid, reinforcing concerns over prolonged droughts. The amplification of both wet and dry extremes aligns with global climate model projections indicating more erratic precipitation patterns due to climate change [20,21] (High confidence). 1.2 Seasonal rainfall trends Seasonal rainfall patterns in the Brazzaville region are undergoing significant transformations. Figure 4 presents a comparative analysis of precipitation trends for the historical period, mid-century projections ( FutureOne ), and late-century projections ( FutureTwo ), distinguishing between the wet and dry seasons. These seasonal shifts complement the previously observed monthly rainfall variations, offering a broader perspective. The historical period exhibits again a well-defined bimodal rainfall regime, with peak precipitation occurring in April and November, consistent with the earlier box plot analysis. Future projections ( FutureOne and FutureTwo ) suggest notable changes in rainfall distribution. April and November experience higher precipitation levels in FutureOne , while FutureTwo further amplifies this trend, confirming the hypothesis of wetter wet seasons (High confidence). However, some months, such as January and May, show a slight decrease in precipitation under future scenarios, suggesting a possible seasonal shift or redistribution of rainfall intensity. This redistribution aligns with the previously observed increase in rainfall variability in the box plots. The growing spread of rainfall values and the increased presence of outliers suggest that extreme precipitation events, such as heavy downpours, may become more frequent in the wet season. Such changes could exacerbate flood risks, soil erosion, and water management challenges, necessitating enhanced flood control and land-use planning. The historical data confirm a distinct dry season from June to August, with a slight recovery in September. Future projections indicate an intensification of drought conditions, particularly in June and July, where median precipitation values decline. This aligns with the earlier box plot findings, which suggested lower median rainfall levels in the dry months for future scenarios. Interestingly, August and September show a slight rebound in rainfall in FutureOne and FutureTwo , suggesting potential changes in seasonal transitions (Medium confidence). This could imply a delay in the onset of the dry season or an earlier arrival of the wet season, adding complexity to water resource planning and agricultural scheduling. 1.3 Annual rainfall trends Annual rainfall trends offer an overarching view of long-term precipitation patterns and their evolution under changing climatic conditions. The bar chart illustrates the mean annual precipitation across three periods: the historical period, mid-century projections ( FutureOne ), and late-century projections ( FutureTwo ). While the differences appear minimal at first glance, a closer examination of seasonal and monthly trends reveals critical insights into shifting climate dynamics. The observed values show a slight decline in total annual precipitation over time, with the historical period recording an average of 160.05 mm, decreasing to 158.87 mm in FutureOne and further to 157.71 mm in FutureTwo . Although this reduction is minor, it suggests a gradual decline in overall precipitation, likely driven by seasonal imbalances rather than a uniform decrease across all months. This finding aligns with the previously analysed seasonal trends, where the dry season showed increasing aridity, particularly in June and July. The slight annual reduction does not imply a steady decline in rainfall throughout the year but rather reflects a redistribution of precipitation, with wetter wet seasons and drier dry seasons balancing each other (Medium confidence). The relatively stable annual totals contrast with heightened variability in seasonal and monthly distributions, showing an increase in extreme precipitation events and greater uncertainty in both peak and dry months. This trend has significant implications, as more intense rainfall during peak wet months could lead to flash floods and urban drainage challenges, while prolonged dry spells may worsen water shortages and impact agriculture. Additionally, greater interannual variability means that some years may experience significantly wetter or drier conditions than the mean suggests, complicating climate adaptation strategies. 1.4 Future rainfall differences Projected changes in rainfall patterns over the Brazzaville region indicate notable variations across different months, with implications for water availability, agriculture, and overall climate resilience. The comparative bar chart in Figure 5 presents monthly precipitation levels for three distinct periods: the historical baseline, mid-century projections ( FutureOne ), and late-century projections ( FutureTwo ). A key observation is the shift in precipitation intensity, where some months experience increases while others decline. Notably, April, October, and November show relatively stable or slightly increased precipitation in the future projections, with FutureTwo displaying the highest values in November (273.05 mm) and April (231.38 mm) compared to the historical baseline. This trend suggests a potential intensification of wet season peaks, which could contribute to a higher risk of flooding and extreme rainfall events. Conversely, June and July show a marked decline in precipitation, with FutureTwo recording as low as 38.69 mm in July, compared to 50.51 mm historically. This reduction indicates a likely intensification of the dry season, which may lead to prolonged drought periods, negatively impacting water availability for agriculture and urban supply. The reduced rainfall in these months aligns with broader regional climate projections, suggesting a stronger precipitation trend seasonality. The transition months, particularly March and September, show more moderate declines, with precipitation remaining relatively close to historical levels. However, the overall variability between wetter and drier months is projected to increase, making rainfall patterns less predictable. This increasing variability underscores the need for adaptive water management strategies, particularly in urban planning, flood control infrastructure, and agricultural scheduling. 1.5 Annual rainfall returns interval The analysis of annual rainfall return intervals (Fig. 6) provides insights into the frequency and intensity of extreme precipitation events over historical, mid-century, and late-century periods. The three probability plots illustrate rainfall values against their return periods, offering a statistical perspective on how often specific rainfall amounts can be expected. In the historical period, extreme rainfall events appear more frequent, with maximum observed values reaching approximately 45 mm for rare return periods. The plotted points align well with the fitted distribution, indicating a reliable historical trend. For the mid-century projections, the general distribution of rainfall events remains similar, but the maximum rainfall recorded for extreme return periods slightly decreases compared to historical values. The plot suggests a shift towards reduced intensity of extreme rainfall events, with peak values around 40 mm for rare occurrences. This suggests that while variability persists, mid-century climate scenarios may lead to slightly less extreme precipitation events. By the late century, a significant decline in maximum annual rainfall events is observed, with extreme precipitation values reaching only 24 mm for the longest return periods. This substantial reduction indicates a decrease in extreme rainfall magnitude over time, potentially altering flood risks and water availability in the region. The narrowing of the confidence bands in the late-century plot further suggests a shift towards more predictable but less intense precipitation extremes. Overall, the results indicate that while rainfall extremes are a defining characteristic of the region’s climate, their intensity is projected to diminish towards the late century. This has critical implications for hydrological planning, infrastructure development, and water resource management, as future extreme events may not reach the historically observed magnitudes. However, potential increases in the frequency of moderate events should also be considered in climate adaptation strategies 1.6 Interannual variability In all three scenarios, climate fluctuations vary from year to year. The interannual variability of precipitation, measured by the standard deviation, shows significant changes between the historical and future periods. Compared to the historical period, with a mean standard deviation of 51.53, the mid-century period shows slightly greater variability, with a mean standard deviation of 56.09, showing more variable annual precipitation from one year to the next. This suggests that some years will experience significantly wetter or drier conditions than the average, reflecting natural climate variability influenced by atmospheric phenomena such as El Niño-Southern Oscillation (ENSO), which significantly influence rainfall distribution in Central Africa. In contrast, the end-of-century period shows a decrease in this variability with a mean standard deviation of 48.33, suggesting a reduction in interannual variability and a potential stabilization of annual precipitation. While this could imply more consistent rainfall levels from year to year, it may also signal prolonged dry periods or a more concentrated wet season, which could impact seasonal water availability. The projected reduction in variability does not necessarily indicate climate stability but rather a shift in precipitation dynamics that requires further investigation. . Thus, although interannual variability increases slightly in the medium term, it finally decreases in the long term, showing a change in future trends. 2. Temperature trends 2.1 Variation in temperatures over the three periods in terms of mean, median, and standard deviation Figure 7 presents the historical (1981–2020), mid-century (2031–2060), and late-century (2071–2100) monthly temperature distributions, showing a clear warming trend over time. The warming is similar for the different scenarios over the first four months of each period. In the historical period, median temperatures range from approximately 25°C to 27.5°C [18,20], with the highest values observed between February and May and the lowest between June and August. By mid-century, median temperatures shift upward, ranging from about 27.5°C to 29.5°C, showing a warming of approximately 2°C, confirming previous studies. Additionally, the spread of temperatures increases slightly, suggesting greater variability. In the late-century period, further warming is evident, with median values ranging from around 29.5°C to 31°C, showing an additional increase of about 1.5°C from the mid-century period. The interquartile ranges are still similar across the three periods, but the upper-temperature extremes become more pronounced, as seen in the higher number of outliers. This consistent increase in both median and extreme temperatures suggests a significant long-term warming trend, which could have serious implications for heat stress, ecosystem stability, and resource management. 2.2 Seasonal temperature trends The analysis of seasonal temperature trends reveals a clear and concerning pattern of progressive warming across both the rainy and dry seasons. In the rainy season, historical temperatures typically range between 25°C and 27.5°C, with noticeable increases in both future projections (FutureOne and FutureTwo). The warming is most pronounced in March, April, and May, months that traditionally mark peak temperatures before the mid-year cooling [18,21]. The difference between the historical and future scenarios suggests a temperature rise of approximately 1.5°C to 2.5°C, with FutureTwo consistently exhibiting the highest values, showing a more extreme warming trajectory. This could lead to intensified heat stress during periods already experiencing high temperatures, affecting both human health and agricultural productivity. In the dry season, moving from June to August, with September considered as a transitioning month, the warming trend is even more striking. Historically, average temperatures in these months hover around 25°C to 27.5°C [18,21], but in future projections, they increase to nearly 30°C, marking a shift of 2°C and 3°C rise. This is particularly concerning for months like July and September, which show the largest jumps in mean temperatures. The implications of such an increase extend beyond mere discomfort; prolonged heat waves, increased evaporation rates, and changes in regional hydrological cycles could significantly impact water availability, biodiversity, and energy demand (High confidence). A warming dry season could exacerbate drought conditions, leading to more severe water shortages and threatening food security. Overall, the seasonal temperature trends show not only a general warming but also an increasing divergence between historical and future conditions [24] (High confidence). The shift is more pronounced in the dry season, which traditionally provides a period of relative thermal relief but is now projected to experience more extreme heat. 2.3 Changes in annual temperatures The analysis of annual temperature changes highlights a clear warming trend over time (Fig. 9), with a substantial increase in median temperatures from the historical period to mid-century and late-century projections (high confidence). The historical baseline indicates a median annual temperature of approximately 26°C [19,21], with a relatively narrow interquartile range, suggesting stable temperature variability. By mid-century, the median temperature rises to around 28°C, representing an increase of approximately 2°C, with a slightly wider spread, showing greater fluctuations in annual temperatures. This shift signifies a transition to consistently warmer conditions, which could have significant implications for ecosystems, agriculture, and human health. The late-century projections depict an even more dramatic increase, with the median temperature reaching nearly 29.5°C, marking a total rise of about 3.5°C from historical values. The interquartile range remains wide, suggesting increased variability and potential for more frequent extreme heat events. Such warming is likely to exacerbate heat stress, increase energy demands for cooling, and disrupt precipitation patterns. The progressive rise in annual temperatures underscores the urgency for robust climate adaptation strategies, including urban heat mitigation, improved water resource management, and sustainable agricultural practices to cope with intensifying thermal conditions. 2.4 Annual temperature return interval. The return interval analysis highlights a significant upward trend in extreme temperature events over time (Fig. 10), illustrating a future where heat extremes become more frequent and intense [21,24]. Historically, the highest mean temperatures remained below 26.5°C, with extreme events occurring only at long return intervals. However, by mid-century, a substantial increase is evident, with return periods reaching 29°C, signalling a climate where heatwaves will no longer be anomalies but expected occurrences. By the late century, extreme temperatures surpassed 30°C, making what were once exceptional heat events a near-regular phenomenon. This escalation not only reduces the recurrence time of high-temperature extremes but also suggests that traditional adaptation measures may become insufficient. The sharp increase in peak temperatures shows a climate shift where prolonged and more intense heat waves will strain ecosystems, infrastructure, and human health. The narrowing gap between return periods suggests that these extreme conditions will no longer be rare but part of the new climatic norm, reinforcing the urgent need for proactive climate adaptation and mitigation strategies. 2.5 Interannual variability The interannual variability of temperatures, assessed through standard deviations across three climate scenarios, highlights an evolving pattern in projected fluctuations. During the historical period, temperature variability remained relatively moderate (σ = 0.507), providing a stable and predictable climate foundation for both ecosystems and human activities. However, projections indicate a growing shift in this stability as we move toward the mid-century. The standard deviation increases significantly to 0.632, reflecting a broader range of temperature fluctuations and the increasing frequency of irregular warm anomalies. By the late century, interannual variability shows a more complex pattern. While the mean standard deviation slightly decreases to 0.514 compared to the mid-century scenario, it stays notably higher than historical values. This suggests alternating periods of intensified and moderate variability, where some years experience unprecedented heat spikes while others see unanticipated fluctuations within a warming climate. Such an unpredictable pattern increases the risk of record-breaking temperatures, prolonged heat waves, and potential climate extremes. This growing variability has critical implications for climate-sensitive sectors. Agriculture must adapt to uncertain growing conditions; water management strategies will need to address both drought risks and excess precipitation, and urban infrastructure must withstand fluctuating thermal stress. 3. Correlation between temperature and precipitation The correlation between temperature and precipitation trends reveals an inverse relationship across monthly, seasonal, and annual timescales. Monthly trends show that the warmest months (March) coincide with lower precipitation, while the wettest months (November) have slightly lower temperatures, suggesting that rising temperatures may contribute to increased evapotranspiration and altered moisture availability. Seasonally, temperature increases are more pronounced during the rainy season, exceeding 3°C by the end of the century [21,22], while precipitation shows an overall decline, particularly during peak wet months, showing that warming intensifies atmospheric instability without necessarily increasing rainfall. Annually, temperatures are projected to rise by +3.5°C by the end of the century, while total precipitation decreases by 1.458% compared to the historical period, reinforcing the negative correlation where higher temperatures contribute to drier conditions, increased drought risks, and altered seasonal rainfall distribution. Scientifically, this pattern aligns with the Clausius-Clapeyron relationship, which suggests that warmer air holds more moisture, leading to more intense but less frequent precipitation events, while increased evapotranspiration accelerates hydrological changes, reducing net precipitation and affecting water availability. Additionally, the decreasing frequency of extreme rainfall events and the intensification of heat waves highlight significant hydrometeorological shifts, with critical implications for agriculture, water resources, and climate adaptation in the region. 4. Potential Impact of Climate Change 4.1 Effects on local ecosystems and wildlife The projected rise in temperatures and changes in precipitation patterns in Brazzaville will significantly affect local ecosystems and biodiversity. The increase in average temperatures, projected to reach +3.5°C by the end of the century [21,22], along with a decline in annual precipitation, suggests a drier and hotter climate that could alter vegetation structure, reduce water availability, and stress terrestrial and aquatic ecosystems [6,25,26]. Higher temperatures and reduced wet-season rainfall may lead to more frequent and prolonged droughts, affecting soil moisture and leading to the potential degradation of forests and wetlands. These conditions could result in increased tree mortality, reduced plant productivity, and changes in species composition, favouring heat and drought-tolerant species over native flora [12,23]. The altered hydrological cycle, with fewer but more intense rainfall events, may also cause increased soil erosion and loss of arable land, further affecting plant growth and reducing habitat quality [21,23]. Wildlife populations may also be at risk, as habitat shifts and food scarcity caused by climate variations will force many species to migrate, adapt, or face population declines [6,21,23]. The Congo River and its surrounding wetlands, crucial for many aquatic and semi-aquatic species, may experience fluctuations in water levels and increased temperatures, affecting fish reproduction cycles and leading to lower biodiversity in aquatic ecosystems. Additionally, increased temperatures and reduced precipitation could enhance the frequency and severity of wildfires, threatening both wildlife and their habitats. 4.2 Socio-economic implications The observed change may have profound socio-economic consequences for communities in the region, particularly affecting vulnerable populations [6]. Rising temperatures and shifting precipitation patterns could intensify extreme weather events in the region, leading to the displacement of populations, destruction of homes, and loss of livelihoods [6,25,26]. If the frequency and severity of floods increase, this will disrupt infrastructure, damage roads and transportation networks, and reduce access to essential services, including healthcare and education. Additionally, extreme weather could exacerbate food insecurity, as prolonged droughts and irregular rainfall patterns might reduce agricultural productivity, affecting smallholder farmers who depend on stable climate conditions for their crops and livestock [6,25,26]. Furthermore, economic activities such as trade, fishing, and tourism might suffer due to changing environmental conditions, reducing income sources for many households. Conclusion This study analysed future climate projections for Brazzaville using CMIP6 models, focusing on temperature and precipitation trends across three periods. The aim was to assess the potential impact of climate change on regional climate variability and provide scientific insights for adaptation and mitigation strategies. The findings reveal a significant and consistent warming trend, with temperatures projected to rise by 3.5°C by the end of the century. Seasonal analysis indicates that both the wet and dry seasons will experience increased temperatures, with the dry season warming more intensely. Similarly, precipitation trends suggest greater variability, with wetter wet seasons and drier dry seasons, leading to an overall redistribution rather than a drastic change in annual totals. Increasing interannual variability, particularly in mid-century projections, indicates more unpredictable climate conditions, with potential consequences for agriculture, water resources, and urban infrastructure. The inverse correlation between temperature and precipitation highlights the likelihood of increased evapotranspiration, leading to drier conditions despite occasional extreme rainfall events. These projected changes underscore the urgency for proactive climate adaptation measures. The expected rise in extreme temperatures and precipitation variability necessitates improved water resource management, resilient agricultural practices, and enhanced urban planning to mitigate flood and heat stress risks. Future research should further refine these projections through regional climate modelling and assess socio-economic impacts to guide policy decisions. Addressing these climate challenges requires integrated efforts to ensure long-term sustainability and climate resilience for Brazzaville and the Republic of Congo. Acronyms and abbreviations CMIP6: Coupled Model Intercomparison Project Phase 6; RGPH-5: Recensement Général de la Population et de l’Habitat (5th Census) ; SSP2-4.5: Shared Socioeconomic Pathway 2-4.5 ; GCMs: General Circulation Models ; CORDEX: Coordinated Regional Climate Downscaling Experiment ; ENSO: El Niño-Southern Oscillation ; IPCC: Intergovernmental Panel on Climate Change ; RCM: Regional Climate Model ; UN: United Nations ; AR5: Fifth Assessment Report; ARDL: Autoregressive Distributed Lag Model ; IQR: Interquartile Range ; σ: Standard Deviation ; NOAA: National Oceanic and Atmospheric Administration ; NASA: National Aeronautics and Space Administration ; CRU: Climate Research Unit ; AGCM: Atmospheric General Circulation Model ; IPSL: Institute Pierre-Simon Laplace ; MPI-ESM: Max Planck Institute Earth System Model ; ACCESS-CM2: Australian Community Climate and Earth-System Simulator – Climate Model 2 ; SPM: Summary for Policymakers ; WG: Working Group. Declarations Funding: This research did not receive any external support. Consent for Publication : The author consents to publication. Conflict of interest : No conflicts of interest are disclosed in this study. Author Contribution The corresponding author is responsible for the entire work, from the project initiation to the final manuscript.The co-author for his count had contributed to data analysis and the revision of the final manuscript.Thank you Acknowledgment I am grateful to Issouf COULIBALY for his significant contributions, intelligent discussions, and steadfast dedication throughout the creation of this research. His knowledge and collaborative attitude were essential to its success. Data Availability The study utilizes historical climate data (1981–2010) sourced from the Climate Explorer database, and future climate projections (2031–2060 and 2071–2100) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), specifically the ACCESS-CM2 global climate model under the SSP2-4.5 emissions scenario. Data processing was conducted using MATLAB, including bias correction (via quantile mapping), statistical downscaling (using CORDEX protocols), and time-series analyses. Key variables analyzed were monthly and seasonal temperature and precipitation, evaluated using descriptive statistics (mean, median, standard deviation), trend analysis, and Pearson correlation. All datasets are publicly available from their respective sources and were processed in accordance with established climate modelling standards. References Hua, W., L. Zhou, S. E. Nicholson, et al. "Assessing Reanalysis Data for Understanding Rainfall Climatology and Variability over Central Equatorial Africa." Climate Dynamics 53 (2019): 651–669. https://doi.org/10.1007/s00382-018-04604-0. Garland, Rebecca M., Mamopeli Matooane, Francois A. Engelbrecht, Mary-Jane M. Bopape, Willem A. Landman, Mogesh Naidoo, Jacobus Van der Merwe, and Caradee Y. Wright. "Regional Projections of Extreme Apparent Temperature Days in Africa and the Related Potential Risk to Human Health." International Journal of Environmental Research and Public Health 12, no. 10 (2015): 12577-12604. https://doi.org/10.3390/ijerph121012577. Intergovernmental Panel on Climate Change (IPCC). Climate Change 2007: Climate Change Impacts, Adaptation, and Vulnerability . Working Group II Contribution to the Fourth Assessment Report. Cambridge: Cambridge University Press, 2007. Urrutia, R., and M. Vuille. "Climate Change Projections for the Tropical Andes Using a Regional Climate Model: Temperature and Precipitation Simulations for the End of the 21st Century." Journal of Geophysical Research: Atmospheres 114 (2009): 1–15. Paparrizos, S., F. Maris, and A. Matzarakis. "Integrated Analysis of Present and Future Responses of Precipitation over Selected Greek Areas with Different Climate Conditions." Atmospheric Research 169, Part A (2016): 199–208. Intergovernmental Panel on Climate Change (IPCC). Climate Change 2013: The Physical Science Basis . Contribution of Working Group I to the Fifth Assessment Report. Cambridge: Cambridge University Press, 2013. https://doi.org/10.1017/CBO9781107415324. Aguilar, E., et al. "Changes in Temperature and Precipitation Extremes in Western Central Africa, Guinea Conakry, and Zimbabwe, 1955–2006." Journal of Geophysical Research 114 (2009): D02115. https://doi.org/10.1029/2008JD011010. United Nations. "Les Impacts du Changement Climatique en Afrique." January 2024. https://news.un.org/fr/story/2024/01/1142472. Zhou, L., Y. Tian, R. Myneni, et al. "Widespread Decline of Congo Rainforest Greenness in the Past Decade." Nature 509 (2014): 86–90. https://doi.org/10.1038/nature13265. Houghton, J. T. Climate Change: The Scientific Basis . Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 2001. Alexander, L. V., X. Zhang, T. C. Peterson, et al. "Global Observed Changes in Daily Climate Extremes of Temperature and Precipitation." Journal of Geophysical Research: Atmospheres 111 (2006): 1–22. https://doi.org/10.1029/2005JD006290. Zwiers, F. W., and X. Z. M. Wehner. "Changes in Temperature and Precipitation Extremes in the CMIP5 Ensemble." Climatic Change (2013): 345–357. https://doi.org/10.1007/s10584-013-0705-8. Droogers, P., and J. Aerts. "Adaptation Strategies to Climate Change and Climate Variability: A Comparative Study between Seven Contrasting River Basins." Physics and Chemistry of the Earth 30 (2005): 339–346. https://doi.org/10.1016/j.pce.2005.06.015. World Bank. Republic of Congo Economic Update, 9th Edition: Climate Change Impacts, Adaptation and Opportunities. Washington, DC: World Bank, 2022. Dargie, G., S. Lewis, I. Lawson, et al. "Age, Extent and Carbon Storage of the Central Congo Basin Peatland Complex." Nature 542 (2017): 86–90. https://doi.org/10.1038/nature21048. Institut National de la Statistique (INS). Rapport Préliminaire du Recensement Général de la Population et de l’Habitat. Brazzaville: INS, 2024. https://ins-congo.cg/rapport-preliminaire/. Tyukavina, Alexandra, et al. "Congo Basin Forest Loss Dominated by Increasing Smallholder Clearing." Science Advances 4 (2018): eaat2993. https://doi.org/10.1126/sciadv.aat2993. Kottek, M., J. Grieser, C. Beck, B. Rudolf, and F. Rubel. "World Map of the Köppen-Geiger Climate Classification Updated." Meteorologische Zeitschrift 15, no. 3 (2006): 259–263. http://dx.doi.org/10.1127/0941-2948/2006/0130. Climate Data Organization. "Climate Statistics for Brazzaville, Congo-Brazzaville." Accessed 2024. https://fr.climate-data.org/afrique/congo-brazzaville/brazzaville/brazzaville-4600/. Tamoffo, Alain T., Alessandro Dosio, Torsten Weber, and Derbetini A. Vondou. "Dynamic and Thermodynamic Contributions to Rainfall Changes in the Congo Basin: Evaluation of the Impact of an RCM’s Formulation." Preprints (December 2023). https://doi.org/10.20944/preprints202312.0226.v1. World Bank. Climate Change Knowledge Portal for the Republic of Congo. World Bank Group, 2023. https://climateknowledgeportal.worldbank.org/country/congo-republic. Adisa, Omolola M., Muthoni Masinde, Joel O. Botai, and Christina M. Botai. "Bibliometric Analysis of Methods and Tools for Drought Monitoring and Prediction in Africa." Sustainability 12, no. 16 (2020): 6516. https://doi.org/10.3390/su12166516. Bouka-Biona, C., and M. Mpounza. "Impact des Changements Climatiques Actuels et Attendus en République du Congo." Colloque SIFEE , May 26–29, 2009. Christensen, J. H., B. Hewitson, A. Busuioc, et al. "Regional Climate Projections." In Climate Change 2007: The Physical Science Basis , edited by Susan Solomon et al., 851–853. Cambridge: Cambridge University Press, 2007. Mbingui, C. "Climate Change and Agricultural Yield in the Republic of Congo: An Analysis Using the ARDL Approach." Theoretical Economics Letters 12 (2022): 1903–1920. https://doi.org/10.4236/tel.2022.126102. Solomon, R., B. Simane, and B. Zaitchik. "The Impact of Climate Change on Agriculture Production in Ethiopia: Application of a Dynamic Computable General Equilibrium Model." American Journal of Climate Change 10, no. 1 (2021): 32–50. https://doi.org/10.4236/ajcc.2021.101003. Additional Declarations No competing interests reported. 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MPIERE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYDCCAwdApAUQMzYACRsQo/EAEVokYFrSwAwCWhhgWsDgMJIgDsB38PCxzwUVEnK6s5sbH1dUnLdb234YaEuNTTQuLZIHjiXPnnFGwtjszsFmwzNnbidvO5MI1HIsLbcBhxaDA2eMmXnbJBK33Uhsk2xsu51sdgCohbHhMAEt/yTqgVrafzb+O5dsdv4hMVoaJBLMgLYwNjYcsAMy8GsB+YWZ55iE4TagXyQbjiUD9QJtScDjF74bhw8z89TYyJvdbn/4saHGzt7sfPrDBx9qbHBqYZA4AGNAqESwygRcykGAH2YYVIs9PsWjYBSMglEwMgEAOjNsVIjIKbMAAAAASUVORK5CYII=","orcid":"","institution":"Pan African University","correspondingAuthor":true,"prefix":"","firstName":"Djornele","middleName":"","lastName":"MPIERE","suffix":""},{"id":452081620,"identity":"95721e97-b8c5-40c3-8a6a-ed9c8a362be5","order_by":1,"name":"Issouf COULIBALY","email":"","orcid":"","institution":"Pan African University","correspondingAuthor":false,"prefix":"","firstName":"Issouf","middleName":"","lastName":"COULIBALY","suffix":""}],"badges":[],"createdAt":"2025-04-21 13:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6496514/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6496514/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82086731,"identity":"74d33f1e-986c-423a-beb0-5a03cde95796","added_by":"auto","created_at":"2025-05-06 15:17:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":214109,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic locations of the Study Area.\u003c/p\u003e","description":"","filename":"image1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6496514/v1/237db6672eec35b9b3311db3.jpg"},{"id":82086733,"identity":"0f2b70bd-658f-4970-a308-b107e2008998","added_by":"auto","created_at":"2025-05-06 15:17:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46021,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of 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trends.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6496514/v1/8c2955513a54486b20ec9d78.png"},{"id":82086736,"identity":"83b4f011-7e49-4bac-9e4c-c6ed9fba3322","added_by":"auto","created_at":"2025-05-06 15:17:09","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":40006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFuture rainfall differences.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6496514/v1/630070eb6a726acca6675e9e.jpg"},{"id":82087978,"identity":"adcb8fb2-8f43-4a0b-ba2d-bcf9dcf80073","added_by":"auto","created_at":"2025-05-06 15:33:09","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":164711,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAnnual rainfall return interval.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6496514/v1/065c2c1fc553e57a11ce2a62.jpeg"},{"id":82087416,"identity":"9bbec933-f33d-4989-ba05-b171a8851b33","added_by":"auto","created_at":"2025-05-06 15:25:09","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":135073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTemperature trends.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6496514/v1/9187d58ac1b45c94f692dff2.jpeg"},{"id":82087418,"identity":"7a8e6d0d-75aa-4f68-98a3-0159cc77bbcf","added_by":"auto","created_at":"2025-05-06 15:25:09","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":104545,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSeasonal temperature trends.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6496514/v1/9e1f06e134eecc0ddb5a2303.jpg"},{"id":82086738,"identity":"e3ec4c77-9c70-4832-b1bc-0e52e38dc77c","added_by":"auto","created_at":"2025-05-06 15:17:09","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":48856,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual temperature change.\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6496514/v1/6c694ed13d7fa9fa16fdb6c4.jpeg"},{"id":82086747,"identity":"7cb87d7d-55a0-48a4-afcb-2a25034ce967","added_by":"auto","created_at":"2025-05-06 15:17:09","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":173037,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAnnual temperature return interval.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6496514/v1/04e029b62c2e5eb28eb2a45c.jpeg"},{"id":86031446,"identity":"f7e0a9e6-6c09-42ce-8e3e-2f398f8be60d","added_by":"auto","created_at":"2025-07-04 14:23:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2002202,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6496514/v1/6bb85258-9632-4861-9085-2b98b89bc40e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Projected Temperature and Precipitation Changes in Brazzaville, Republic of Congo: A CMIP6-Based Analysis","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; The research uses CMIP6 climate projections under the SSP2-4.5 scenario.\u003c/p\u003e\u003cp\u003e\u0026bull; It focuses on future climate change impacts on temperature and precipitation in Brazzaville.\u003c/p\u003e\u003cp\u003e\u0026bull; Climate change impacts are analysed across three-time horizons: historical (1981\u0026ndash;2010), mid-century (2031\u0026ndash;2060), and late-century (2071\u0026ndash;2100).\u003c/p\u003e\u003cp\u003e\u0026bull; The study applies statistical downscaling, bias correction, and trend analysis using observed climate data for model calibration.\u003c/p\u003e"},{"header":"I. Introduction","content":"\u003cp\u003eClimate change refers to variations in climate variables, particularly temperature and precipitation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The global average temperature has increased by approximately 0.74\u0026deg;C between 1906 and 2005 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], with two major warming phases: from 1910 to the 1940s and more significantly from the 1970s to 2005. Rising temperatures lead to increased evapotranspiration and alter global precipitation patterns [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These changes influence temperature and precipitation trends, affecting the frequency and intensity of extreme events such as floods and droughts at regional and local scales [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The high concentration of greenhouse gases in the atmosphere contributes to rising temperatures and changes in rainfall distribution and intensity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These changes affect monthly and seasonal trends, and the interannual variability of climatic conditions.\u003c/p\u003e \u003cp\u003eThe beginning of the 21st century is recorded as the warmest period [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and climate change impacts are expected to persist with continued increases in temperature and rainfall variability [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Africa is particularly vulnerable to climate change due to its heavy dependence on climate-sensitive sectors such as agriculture [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The continent is among the most affected regions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Future climate projections for Central Africa indicate increasing temperatures and shifts in precipitation patterns.\u003c/p\u003e \u003cp\u003eSimilarly, future climate projections show significant changes for the Republic of Congo, particularly in the Brazzaville region. Bouka-Biona \u0026amp; Mpounza in 2009 and Cyriaque Mbingui in 2022 suggest that by the mid-to-late 21st century, climate change could lead to increased temperatures and variability in rainfall patterns in the Republic of Congo, posing challenges to water resource management, agriculture, and urban infrastructure.\u003c/p\u003e \u003cp\u003eIPCC reports showed the presence of climate change at the international level based on different scenarios. The current report of the IPCC, which is used for different impact assessment studies, is based on the Shared Socioeconomic Pathways (SSPs) scenarios. It is the latest generation that provides input to climate models, which integrate different socioeconomic development trajectories with greenhouse gas emissions scenarios. These scenarios provide a more comprehensive framework for assessing climate change risks. Five main scenarios have been defined with different socioeconomic pathways. The SSP1-1.9: Assumes very low greenhouse gas emissions, with CO₂ emissions reaching net zero around 2050. Projected global warming is approximately 1.4\u0026deg;C by 2081\u0026ndash;2100. SSP1-2.6: Assumes low greenhouse gas emissions, with CO₂ emissions reaching net zero around 2075. Projected global warming is approximately 1.8\u0026deg;C by 2081\u0026ndash;2100. SSP2-4.5: Assumes intermediate greenhouse gas emissions, with CO₂ emissions remaining around current levels until 2050, then declining but not reaching net zero by 2100. Projected global warming is approximately 2.7\u0026deg;C by 2081\u0026ndash;2100. SSP3-7.0: Assumes high greenhouse gas emissions, with CO₂ emissions doubling by 2100. Projected global warming is approximately 3.6\u0026deg;C by 2081\u0026ndash;2100. SSP5-8.5: Assumes very high greenhouse gas emissions, with CO₂ emissions tripling by 2075. Projected global warming is approximately 4.4\u0026deg;C by 2081\u0026ndash;2100.\u003c/p\u003e \u003cp\u003eClimate change impacts are spatially and temporally variable [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], making localized studies essential for developing effective adaptation strategies. While global climate models (GCMs) have been extensively used, newer climate projections based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) provide improved regional climate assessments.\u003c/p\u003e \u003cp\u003eThis study aims to analyse climate projections for the region of Brazzaville using CMIP6 models, focusing on temperature and precipitation trends for three periods: 1981\u0026ndash;2010 (historical baseline), 2031\u0026ndash;2060 (mid-century), and 2071\u0026ndash;2100 (end of the century). The results will help inform policies and strategies for climate adaptation and mitigation.\u003c/p\u003e \u003cp\u003eThe primary objective of this study is to estimate the potential impact of climate change on future temperature and precipitation patterns in the Brazzaville region. The specific objectives include: (1) Assessing historical climate variability in the region. (2) Analysing projected changes in temperature and precipitation based on the SSP2-4.5 scenario. And (3) Evaluating potential implications for ecosystems, community, and livelihood.\u003c/p\u003e \u003cp\u003eBy providing a robust scientific foundation for climate adaptation strategies, this study aims to support sustainable development efforts in the Republic of Congo. Understanding future climate scenarios will enable policymakers and stakeholders to implement effective measures and reduce vulnerability to climate-related risks.\u003c/p\u003e"},{"header":"II. Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e1.\u0026nbsp; \u0026nbsp;Area of study\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Brazzaville region is the capital city of the Republic of Congo, located in the south of the country, on the right bank of the Congo River. With a population estimated at around 34.9% of the country\u0026apos;s 6,142,180 inhabitants [16], the city spans over 264 km\u0026sup2; out of the 342,000 km\u0026sup2; of the country [17]. The region is geographically located at -4\u0026deg;7\u0026quot;48\u0026quot; and -4\u0026deg;20\u0026apos;24\u0026quot; south latitude, and 15\u0026deg;7\u0026apos;12\u0026quot; and 15\u0026deg;19\u0026apos;48\u0026quot; east longitude. According to the K\u0026ouml;ppen-Geiger classification, the microclimate reflects a humid tropical climate [19], characterized by seasonal variability with two distinct seasons, a dry season from June to September and a rainy season from October to May, with two wetter periods between March-April and November-December [17,19]. During the dry season, average temperatures vary between 18\u0026deg;C and 38\u0026deg;C, while during the rainy season they fluctuate between 22\u0026deg;C and 33\u0026deg;C [19,21]. Rainfall totals 1,095 millimetres per year, making it abundant [19]. Soils are generally ferralitic and hydromorphic, poor in nutrients and organic matter. They are affected by a steady decline in fertility, increased acidification, and susceptibility to water and wind erosion, mainly due to land use and urbanization. The region often experiences erosion, flooding, and other events without well-documented empirical evidence of climate change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.\u0026nbsp; \u0026nbsp;Materials and methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1\u0026nbsp;Research design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adopts a mixed-methods approach, integrating both quantitative and qualitative methods to analyse future climate projections in Brazzaville. The research combines climate modelling and statistical analysis to assess trends in temperature and precipitation over different periods. key periods include historical baseline (1981\u0026ndash;2010), mid-century or future One period (2031\u0026ndash;2060), and late-century or future Two period (2071\u0026ndash;2100). By employing statistical metrics (mean, median, standard deviation, and trend analysis) and contextual insights from the literature review, the study ensures a comprehensive understanding of climate variability and its probable implications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2\u0026nbsp;Climate Variables and Metrics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKey climate indicators analysed included temperature and precipitation, both assessed using mean, median, and standard deviation. Trend analysis was conducted across monthly, seasonal, and interannual timescales to identify patterns and long-term changes. Additionally, correlation analysis was performed to examine the relationship between temperature and precipitation variations. Data processing and visualization were performed to enable statistical analysis, trend detection, and spatial representation.\u0026nbsp;Historical climate data for the period 1981\u0026ndash;2010 were sourced from Climate Explorer, while future projections for 2031\u0026ndash;2060 and 2071\u0026ndash;2100 were extracted from CMIP6 simulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3\u0026nbsp;Data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data analysis was conducted using MATLAB, which helped with statistical computations, trend analysis, correlation assessments, and graphical visualizations of climate variables. Climate projections were obtained from CMIP6 (Coupled Model Intercomparison Project Phase 6), using the ACCESS-CM2 model. These models were selected based on their high spatial resolution and ability to simulate regional climate processes accurately. The study adopted the SSP2-4.5 (Shared Socioeconomic Pathway 2-4.5) scenario, which represents a moderate emissions trajectory suitable for the economic and environmental context of the Republic of Congo.\u003c/p\u003e\n\u003cp\u003eThe data analysis followed a systematic processing approach, beginning with data preprocessing, where raw climate datasets were cleaned, formatted, and structured. Missing data were handled using statistical interpolation techniques, and spatial and temporal consistency was ensured. Bias correction was applied to CMIP6 model outputs to minimize systematic errors, following CORDEX approaches. After preprocessing, trend analysis was performed to assess long-term changes in temperature and precipitation across monthly, seasonal, and interannual timescales. Time-series decomposition techniques were used to separate long-term trends, seasonal cycles, and residual variability.\u003c/p\u003e\n\u003cp\u003eTo evaluate climate variability, Pearson correlation analysis and linear regression models were applied to examine relationships between temperature and precipitation over different time frames. Then, comparative trend graphs were generated to highlight differences between historical and future climate conditions.\u003c/p\u003e"},{"header":"III. Results and discussion","content":"\u003cp\u003eThe analysis of future climate projections reveals significant temperature and precipitation pattern shifts, with implications for long-term climate variability and extreme weather events. By examining historical trends (1981\u0026ndash;2010) alongside mid-century (2031\u0026ndash;2060) and late-century (2071\u0026ndash;2100) projections, the results highlight a progressive increase in temperature, modifications in seasonal rainfall distribution, and growing interannual variability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.\u0026nbsp; \u0026nbsp;Precipitation trends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1\u0026nbsp;Variation in precipitation over the three periods in terms of mean, median, and standard deviation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3 shows changes in the relative deviation of monthly cumulative precipitation compared to the historical period for the three scenarios over the 21st century, with distinct trends in monthly means, medians, and standard deviations. It shows increased variability with rainfall trends unevenly distributed over the three periods.\u003c/p\u003e\n\u003cp\u003eThe historical data (1981\u0026ndash;2010) exhibit a distinct bimodal rainfall pattern, characteristic of the region\u0026rsquo;s equatorial climate. Peak precipitation occurs between March-May and October-December, while a pronounced dry season is observed from June to August. The interquartile range (IQR) of rainfall distribution shows substantial variability, particularly during the peak rainfall months of April, October, and November. The presence of outliers further indicates instances of extreme precipitation events, highlighting the inherent fluctuations in the historical climate.\u003c/p\u003e\n\u003cp\u003eMid-century projections (2031\u0026ndash;2060) indicate a shift in rainfall distribution, with notable changes in seasonal patterns. The median rainfall values during the dry season (June-August) appear lower, suggesting a potential intensification of drought conditions. Conversely, rainfall variability during the wettest months increases, as evidenced by the widened IQR and the frequency of outliers. This suggests an increased likelihood of extreme weather events, including heavy rainfall episodes interspersed with drier periods, which could have significant implications for water resource management, agriculture, and disaster preparedness (High confidence).\u003c/p\u003e\n\u003cp\u003eBy the late century (2071\u0026ndash;2100), the projections reveal a more pronounced alteration in rainfall patterns. The wet season experiences higher median precipitation levels, indicating intensified rainfall during peak months. The extended whiskers and increased number of outliers suggest heightened interannual fluctuations, implying greater uncertainty in seasonal rainfall distribution. The dry season remains markedly arid, reinforcing concerns over prolonged droughts. The amplification of both wet and dry extremes aligns with global climate model projections indicating more erratic precipitation patterns due to climate change [20,21] (High confidence).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2\u0026nbsp;Seasonal rainfall trends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeasonal rainfall patterns in the Brazzaville region are undergoing significant transformations. Figure 4 presents a comparative analysis of precipitation trends for the historical period, mid-century projections (\u003cem\u003eFutureOne\u003c/em\u003e), and late-century projections (\u003cem\u003eFutureTwo\u003c/em\u003e), distinguishing between the wet and dry seasons. These seasonal shifts complement the previously observed monthly rainfall variations, offering a broader perspective.\u003c/p\u003e\n\u003cp\u003eThe historical period exhibits again a well-defined bimodal rainfall regime, with peak precipitation occurring in April and November, consistent with the earlier box plot analysis. Future projections (\u003cem\u003eFutureOne\u003c/em\u003e and \u003cem\u003eFutureTwo\u003c/em\u003e) suggest notable changes in rainfall distribution. April and November experience higher precipitation levels in \u003cem\u003eFutureOne\u003c/em\u003e, while \u003cem\u003eFutureTwo\u003c/em\u003e further amplifies this trend, confirming the hypothesis of wetter wet seasons (High confidence). However, some months, such as January and May, show a slight decrease in precipitation under future scenarios, suggesting a possible seasonal shift or redistribution of rainfall intensity.\u003c/p\u003e\n\u003cp\u003eThis redistribution aligns with the previously observed increase in rainfall variability in the box plots. The growing spread of rainfall values and the increased presence of outliers suggest that extreme precipitation events, such as heavy downpours, may become more frequent in the wet season. Such changes could exacerbate flood risks, soil erosion, and water management challenges, necessitating enhanced flood control and land-use planning.\u003c/p\u003e\n\u003cp\u003eThe historical data confirm a distinct dry season from June to August, with a slight recovery in September. Future projections indicate an intensification of drought conditions, particularly in June and July, where median precipitation values decline. This aligns with the earlier box plot findings, which suggested lower median rainfall levels in the dry months for future scenarios. Interestingly, August and September show a slight rebound in rainfall in \u003cem\u003eFutureOne\u003c/em\u003e and \u003cem\u003eFutureTwo\u003c/em\u003e, suggesting potential changes in seasonal transitions (Medium confidence). This could imply a delay in the onset of the dry season or an earlier arrival of the wet season, adding complexity to water resource planning and agricultural scheduling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3\u0026nbsp;Annual rainfall trends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnnual rainfall trends offer an overarching view of long-term precipitation patterns and their evolution under changing climatic conditions. The bar chart illustrates the mean annual precipitation across three periods: the historical period, mid-century projections (\u003cem\u003eFutureOne\u003c/em\u003e), and late-century projections (\u003cem\u003eFutureTwo\u003c/em\u003e). While the differences appear minimal at first glance, a closer examination of seasonal and monthly trends reveals critical insights into shifting climate dynamics. The observed values show a slight decline in total annual precipitation over time, with the historical period recording an average of 160.05 mm, decreasing to 158.87 mm in \u003cem\u003eFutureOne\u003c/em\u003e and further to 157.71 mm in \u003cem\u003eFutureTwo\u003c/em\u003e. Although this reduction is minor, it suggests a gradual decline in overall precipitation, likely driven by seasonal imbalances rather than a uniform decrease across all months. This finding aligns with the previously analysed seasonal trends, where the dry season showed increasing aridity, particularly in June and July. The slight annual reduction does not imply a steady decline in rainfall throughout the year but rather reflects a redistribution of precipitation, with wetter wet seasons and drier dry seasons balancing each other (Medium confidence). The relatively stable annual totals contrast with heightened variability in seasonal and monthly distributions, showing an increase in extreme precipitation events and greater uncertainty in both peak and dry months. This trend has significant implications, as more intense rainfall during peak wet months could lead to flash floods and urban drainage challenges, while prolonged dry spells may worsen water shortages and impact agriculture. Additionally, greater interannual variability means that some years may experience significantly wetter or drier conditions than the mean suggests, complicating climate adaptation strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4\u0026nbsp;Future rainfall differences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProjected changes in rainfall patterns over the Brazzaville region indicate notable variations across different months, with implications for water availability, agriculture, and overall climate resilience. The comparative bar chart in Figure 5 presents monthly precipitation levels for three distinct periods: the historical baseline, mid-century projections (\u003cem\u003eFutureOne\u003c/em\u003e), and late-century projections (\u003cem\u003eFutureTwo\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eA key observation is the shift in precipitation intensity, where some months experience increases while others decline. Notably, April, October, and November show relatively stable or slightly increased precipitation in the future projections, with \u003cem\u003eFutureTwo\u003c/em\u003e displaying the highest values in November (273.05 mm) and April (231.38 mm) compared to the historical baseline. This trend suggests a potential intensification of wet season peaks, which could contribute to a higher risk of flooding and extreme rainfall events.\u003c/p\u003e\n\u003cp\u003eConversely, June and July show a marked decline in precipitation, with \u003cem\u003eFutureTwo\u003c/em\u003e recording as low as 38.69 mm in July, compared to 50.51 mm historically. This reduction indicates a likely intensification of the dry season, which may lead to prolonged drought periods, negatively impacting water availability for agriculture and urban supply. The reduced rainfall in these months aligns with broader regional climate projections, suggesting a stronger precipitation trend seasonality.\u003c/p\u003e\n\u003cp\u003eThe transition months, particularly March and September, show more moderate declines, with precipitation remaining relatively close to historical levels. However, the overall variability between wetter and drier months is projected to increase, making rainfall patterns less predictable. This increasing variability underscores the need for adaptive water management strategies, particularly in urban planning, flood control infrastructure, and agricultural scheduling.\u003c/p\u003e\n\u003cp\u003e1.5\u0026nbsp;Annual rainfall returns interval\u003c/p\u003e\n\u003cp\u003eThe analysis of annual rainfall return intervals (Fig. 6) provides\u0026nbsp;insights into the frequency and intensity of extreme precipitation events over historical, mid-century, and late-century periods. The three probability plots illustrate rainfall values against their return periods, offering a statistical perspective on how often specific rainfall amounts can be expected.\u003c/p\u003e\n\u003cp\u003eIn the historical period, extreme rainfall events appear more frequent, with maximum observed values reaching approximately 45 mm for rare return periods. The plotted points align well with the fitted distribution, indicating a reliable historical trend. For the mid-century projections, the general distribution of rainfall events remains similar, but the maximum rainfall recorded for extreme return periods slightly decreases compared to historical values. The plot suggests a shift towards reduced intensity of extreme rainfall events, with peak values around 40 mm for rare occurrences. This suggests that while variability persists, mid-century climate scenarios may lead to slightly less extreme precipitation events.\u003c/p\u003e\n\u003cp\u003eBy the late century, a significant decline in maximum annual rainfall events is observed, with extreme precipitation values reaching only 24 mm for the longest return periods. This substantial reduction indicates a decrease in extreme rainfall magnitude over time, potentially altering flood risks and water availability in the region. The narrowing of the confidence bands in the late-century plot further suggests a shift towards more predictable but less intense precipitation extremes.\u003c/p\u003e\n\u003cp\u003eOverall, the results indicate that while rainfall extremes are a defining characteristic of the region\u0026rsquo;s climate, their intensity is projected to diminish towards the late century. This has critical implications for hydrological planning, infrastructure development, and water resource management, as future extreme events may not reach the historically observed magnitudes. However, potential increases in the frequency of moderate events should also be considered in climate adaptation strategies\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.6\u0026nbsp;Interannual variability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn all three scenarios, climate fluctuations vary from year to year. The interannual variability of precipitation, measured by the standard deviation, shows significant changes between the historical and future periods. Compared to the historical period, with a mean standard deviation of 51.53, the mid-century period shows slightly greater variability, with a mean standard deviation of 56.09, showing more variable annual precipitation from one year to the next. This suggests that some years will experience significantly wetter or drier conditions than the average, reflecting natural climate variability influenced by atmospheric phenomena such as El Ni\u0026ntilde;o-Southern Oscillation (ENSO), which significantly influence rainfall distribution in Central Africa. In contrast, the end-of-century period shows a decrease in this variability with a mean standard deviation of 48.33, suggesting a reduction in interannual variability and a potential stabilization of annual precipitation. While this could imply more consistent rainfall levels from year to year, it may also signal prolonged dry periods or a more concentrated wet season, which could impact seasonal water availability. The projected reduction in variability does not necessarily indicate climate stability but rather a shift in precipitation dynamics that requires further investigation.\u003c/p\u003e\n\u003cp\u003e. Thus, although interannual variability increases slightly in the medium term, it finally decreases in the long term, showing a change in future trends.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.\u0026nbsp; \u0026nbsp;Temperature trends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1\u0026nbsp;Variation in temperatures over the three periods in terms of mean, median, and standard deviation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 7 presents the historical (1981\u0026ndash;2020), mid-century (2031\u0026ndash;2060), and late-century (2071\u0026ndash;2100) monthly temperature distributions, showing a clear warming trend over time. The warming is similar for the different scenarios over the first four months of each period. In the historical period, median temperatures range from approximately 25\u0026deg;C to 27.5\u0026deg;C [18,20], with the highest values observed between February and May and the lowest between June and August. By mid-century, median temperatures shift upward, ranging from about 27.5\u0026deg;C to 29.5\u0026deg;C, showing a warming of approximately 2\u0026deg;C, confirming previous studies. Additionally, the spread of temperatures increases slightly, suggesting greater variability. In the late-century period, further warming is evident, with median values ranging from around 29.5\u0026deg;C to 31\u0026deg;C, showing an additional increase of about 1.5\u0026deg;C from the mid-century period. The interquartile ranges are still similar across the three periods, but the upper-temperature extremes become more pronounced, as seen in the higher number of outliers. This consistent increase in both median and extreme temperatures suggests a significant long-term warming trend, which could have serious implications for heat stress, ecosystem stability, and resource management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2\u0026nbsp;Seasonal temperature trends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of seasonal temperature trends reveals a clear and concerning pattern of progressive warming across both the rainy and dry seasons. In the rainy season, historical temperatures typically range between 25\u0026deg;C and 27.5\u0026deg;C, with noticeable increases in both future projections (FutureOne and FutureTwo). The warming is most pronounced in March, April, and May, months that traditionally mark peak temperatures before the mid-year cooling [18,21]. The difference between the historical and future scenarios suggests a temperature rise of approximately 1.5\u0026deg;C to 2.5\u0026deg;C, with FutureTwo consistently exhibiting the highest values, showing a more extreme warming trajectory. This could lead to intensified heat stress during periods already experiencing high temperatures, affecting both human health and agricultural productivity.\u003c/p\u003e\n\u003cp\u003eIn the dry season, moving from June to August, with September considered as a transitioning month, the warming trend is even more striking. Historically, average temperatures in these months hover around 25\u0026deg;C to 27.5\u0026deg;C [18,21], but in future projections, they increase to nearly 30\u0026deg;C, marking a shift of 2\u0026deg;C and 3\u0026deg;C rise. This is particularly concerning for months like July and September, which show the largest jumps in mean temperatures. The implications of such an increase extend beyond mere discomfort; prolonged heat waves, increased evaporation rates, and changes in regional hydrological cycles could significantly impact water availability, biodiversity, and energy demand (High confidence). A warming dry season could exacerbate drought conditions, leading to more severe water shortages and threatening food security.\u003c/p\u003e\n\u003cp\u003eOverall, the seasonal temperature trends show not only a general warming but also an increasing divergence between historical and future conditions [24] (High confidence). The shift is more pronounced in the dry season, which traditionally provides a period of relative thermal relief but is now projected to experience more extreme heat.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3\u0026nbsp;Changes in annual temperatures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of annual temperature changes highlights a clear warming trend over time (Fig. 9), with a substantial increase in median temperatures from the historical period to mid-century and late-century projections (high confidence). The historical baseline indicates a median annual temperature of approximately 26\u0026deg;C [19,21], with a relatively narrow interquartile range, suggesting stable temperature variability. By mid-century, the median temperature rises to around 28\u0026deg;C, representing an increase of approximately 2\u0026deg;C, with a slightly wider spread, showing greater fluctuations in annual temperatures. This shift signifies a transition to consistently warmer conditions, which could have significant implications for ecosystems, agriculture, and human health.\u003c/p\u003e\n\u003cp\u003eThe late-century projections depict an even more dramatic increase, with the median temperature reaching nearly 29.5\u0026deg;C, marking a total rise of about 3.5\u0026deg;C from historical values. The interquartile range remains wide, suggesting increased variability and potential for more frequent extreme heat events. Such warming is likely to exacerbate heat stress, increase energy demands for cooling, and disrupt precipitation patterns. The progressive rise in annual temperatures underscores the urgency for robust climate adaptation strategies, including urban heat mitigation, improved water resource management, and sustainable agricultural practices to cope with intensifying thermal conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4\u0026nbsp;Annual temperature return interval.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe return interval analysis highlights a significant upward trend in extreme temperature events over time (Fig. 10), illustrating a future where heat extremes become more frequent and intense [21,24]. Historically, the highest mean temperatures remained below 26.5\u0026deg;C, with extreme events occurring only at long return intervals. However, by mid-century, a substantial increase is evident, with return periods reaching 29\u0026deg;C, signalling a climate where heatwaves will no longer be anomalies but expected occurrences.\u003c/p\u003e\n\u003cp\u003eBy the late century, extreme temperatures surpassed 30\u0026deg;C, making what were once exceptional heat events a near-regular phenomenon. This escalation not only reduces the recurrence time of high-temperature extremes but also suggests that traditional adaptation measures may become insufficient. The sharp increase in peak temperatures shows a climate shift where prolonged and more intense heat waves will strain ecosystems, infrastructure, and human health. The narrowing gap between return periods suggests that these extreme conditions will no longer be rare but part of the new climatic norm, reinforcing the urgent need for proactive climate adaptation and mitigation strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5\u0026nbsp;Interannual variability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe interannual variability of temperatures, assessed through standard deviations across three climate scenarios, highlights an evolving pattern in projected fluctuations. During the historical period, temperature variability remained relatively moderate (\u0026sigma; = 0.507), providing a stable and predictable climate foundation for both ecosystems and human activities. However, projections indicate a growing shift in this stability as we move toward the mid-century. The standard deviation increases significantly to 0.632, reflecting a broader range of temperature fluctuations and the increasing frequency of irregular warm anomalies.\u003c/p\u003e\n\u003cp\u003eBy the late century, interannual variability shows a more complex pattern. While the mean standard deviation slightly decreases to 0.514 compared to the mid-century scenario, it stays notably higher than historical values. This suggests alternating periods of intensified and moderate variability, where some years experience unprecedented heat spikes while others see unanticipated fluctuations within a warming climate. Such an unpredictable pattern increases the risk of record-breaking temperatures, prolonged heat waves, and potential climate extremes.\u003c/p\u003e\n\u003cp\u003eThis growing variability has critical implications for climate-sensitive sectors. Agriculture must adapt to uncertain growing conditions; water management strategies will need to address both drought risks and excess precipitation, and urban infrastructure must withstand fluctuating thermal stress.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u0026nbsp; \u0026nbsp;Correlation between temperature and precipitation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe correlation between temperature and precipitation trends reveals an inverse relationship across monthly, seasonal, and annual timescales. Monthly trends show that the warmest months (March) coincide with lower precipitation, while the wettest months (November) have slightly lower temperatures, suggesting that rising temperatures may contribute to increased evapotranspiration and altered moisture availability. Seasonally, temperature increases are more pronounced during the rainy season, exceeding 3\u0026deg;C by the end of the century [21,22], while precipitation shows an overall decline, particularly during peak wet months, showing that warming intensifies atmospheric instability without necessarily increasing rainfall. Annually, temperatures are projected to rise by\u0026nbsp;+3.5\u0026deg;C\u0026nbsp;by the end of the century, while total precipitation decreases by\u0026nbsp;1.458%\u0026nbsp;compared to the historical period, reinforcing the negative correlation where higher temperatures contribute to drier conditions, increased drought risks, and altered seasonal rainfall distribution. Scientifically, this pattern aligns with the\u0026nbsp;Clausius-Clapeyron relationship, which suggests that warmer air holds more moisture, leading to more intense but less frequent precipitation events, while increased evapotranspiration accelerates hydrological changes, reducing net precipitation and affecting water availability. Additionally, the decreasing frequency of extreme rainfall events and the intensification of heat waves highlight significant hydrometeorological shifts, with critical implications for agriculture, water resources, and climate adaptation in the region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.\u0026nbsp; \u0026nbsp;Potential Impact of Climate Change\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 \u003cstrong\u003eEffects on local ecosystems and wildlife\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe projected rise in temperatures and changes in precipitation patterns in Brazzaville will significantly affect local ecosystems and biodiversity. The increase in average temperatures, projected to reach +3.5\u0026deg;C by the end of the century [21,22], along with a decline in annual precipitation, suggests a drier and hotter climate that could alter vegetation structure, reduce water availability, and stress terrestrial and aquatic ecosystems [6,25,26]. Higher temperatures and reduced wet-season rainfall may lead to more frequent and prolonged droughts, affecting soil moisture and leading to the potential degradation of forests and wetlands. These conditions could result in increased tree mortality, reduced plant productivity, and changes in species composition, favouring heat and drought-tolerant species over native flora [12,23]. The altered hydrological cycle, with fewer but more intense rainfall events, may also cause increased soil erosion and loss of arable land, further affecting plant growth and reducing habitat quality [21,23].\u003c/p\u003e\n\u003cp\u003eWildlife populations may also be at risk, as habitat shifts and food scarcity caused by climate variations will force many species to migrate, adapt, or face population declines [6,21,23]. The Congo River and its surrounding wetlands, crucial for many aquatic and semi-aquatic species, may experience fluctuations in water levels and increased temperatures, affecting fish reproduction cycles and leading to lower biodiversity in aquatic ecosystems. Additionally, increased temperatures and reduced precipitation could enhance the frequency and severity of wildfires, threatening both wildlife and their habitats.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2\u0026nbsp;Socio-economic implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe observed change may have profound socio-economic consequences for communities in the region, particularly affecting vulnerable populations [6]. Rising temperatures and shifting precipitation patterns could intensify extreme weather events in the region, leading to the displacement of populations, destruction of homes, and loss of livelihoods [6,25,26]. If the frequency and severity of floods increase, this will disrupt infrastructure, damage roads and transportation networks, and reduce access to essential services, including healthcare and education. Additionally, extreme weather could exacerbate food insecurity, as prolonged droughts and irregular rainfall patterns might reduce agricultural productivity, affecting smallholder farmers who depend on stable climate conditions for their crops and livestock [6,25,26]. Furthermore, economic activities such as trade, fishing, and tourism might suffer due to changing environmental conditions, reducing income sources for many households.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study analysed future climate projections for Brazzaville using CMIP6 models, focusing on temperature and precipitation trends across three periods. The aim was to assess the potential impact of climate change on regional climate variability and provide scientific insights for adaptation and mitigation strategies. The findings reveal a significant and consistent warming trend, with temperatures projected to rise by 3.5\u0026deg;C by the end of the century. Seasonal analysis indicates that both the wet and dry seasons will experience increased temperatures, with the dry season warming more intensely. Similarly, precipitation trends suggest greater variability, with wetter wet seasons and drier dry seasons, leading to an overall redistribution rather than a drastic change in annual totals. Increasing interannual variability, particularly in mid-century projections, indicates more unpredictable climate conditions, with potential consequences for agriculture, water resources, and urban infrastructure. The inverse correlation between temperature and precipitation highlights the likelihood of increased evapotranspiration, leading to drier conditions despite occasional extreme rainfall events.\u003c/p\u003e\n\u003cp\u003eThese projected changes underscore the urgency for proactive climate adaptation measures. The expected rise in extreme temperatures and precipitation variability necessitates improved water resource management, resilient agricultural practices, and enhanced urban planning to mitigate flood and heat stress risks. Future research should further refine these projections through regional climate modelling and assess socio-economic impacts to guide policy decisions. Addressing these climate challenges requires integrated efforts to ensure long-term sustainability and climate resilience for Brazzaville and the Republic of Congo.\u003c/p\u003e"},{"header":"Acronyms and abbreviations","content":"\u003cp\u003eCMIP6: Coupled Model Intercomparison Project Phase 6; RGPH-5: Recensement G\u0026eacute;n\u0026eacute;ral de la Population et de l\u0026rsquo;Habitat (5th Census) ; SSP2-4.5: Shared Socioeconomic Pathway 2-4.5 ; GCMs: General Circulation Models ; CORDEX: Coordinated Regional Climate Downscaling Experiment ; ENSO: El Ni\u0026ntilde;o-Southern Oscillation ; IPCC: Intergovernmental Panel on Climate Change ; RCM: Regional Climate Model ; UN: United Nations ; AR5: Fifth Assessment Report; ARDL: Autoregressive Distributed Lag Model ; IQR: Interquartile Range ; \u0026sigma;: Standard Deviation ; NOAA: National Oceanic and Atmospheric Administration ; NASA: National Aeronautics and Space Administration ; CRU: Climate Research Unit ; AGCM: Atmospheric General Circulation Model ; IPSL: Institute Pierre-Simon Laplace ; MPI-ESM: Max Planck Institute Earth System Model ; \u0026nbsp;ACCESS-CM2: Australian Community Climate and Earth-System Simulator \u0026ndash; Climate Model 2 ; SPM: Summary for Policymakers ; WG: Working Group.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research did not receive any external support.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConsent for Publication\u003c/b\u003e: The author consents to publication.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConflict of interest\u003c/b\u003e: No conflicts of interest are disclosed in this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe corresponding author is responsible for the entire work, from the project initiation to the final manuscript.The co-author for his count had contributed to data analysis and the revision of the final manuscript.Thank you\u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e \u003cp\u003eI am grateful to Issouf COULIBALY for his significant contributions, intelligent discussions, and steadfast dedication throughout the creation of this research. His knowledge and collaborative attitude were essential to its success.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe study utilizes historical climate data (1981\u0026ndash;2010) sourced from the Climate Explorer database, and future climate projections (2031\u0026ndash;2060 and 2071\u0026ndash;2100) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), specifically the ACCESS-CM2 global climate model under the SSP2-4.5 emissions scenario. Data processing was conducted using MATLAB, including bias correction (via quantile mapping), statistical downscaling (using CORDEX protocols), and time-series analyses. Key variables analyzed were monthly and seasonal temperature and precipitation, evaluated using descriptive statistics (mean, median, standard deviation), trend analysis, and Pearson correlation. All datasets are publicly available from their respective sources and were processed in accordance with established climate modelling standards.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHua, W., L. Zhou, S. E. Nicholson, et al. \u0026quot;Assessing Reanalysis Data for Understanding Rainfall Climatology and Variability over Central Equatorial Africa.\u0026quot; \u003cem\u003eClimate Dynamics\u003c/em\u003e 53 (2019): 651\u0026ndash;669. https://doi.org/10.1007/s00382-018-04604-0.\u003c/li\u003e\n\u003cli\u003eGarland, Rebecca M., Mamopeli Matooane, Francois A. Engelbrecht, Mary-Jane M. Bopape, Willem A. Landman, Mogesh Naidoo, Jacobus Van der Merwe, and Caradee Y. 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Lawson, et al. \u0026quot;Age, Extent and Carbon Storage of the Central Congo Basin Peatland Complex.\u0026quot; \u003cem\u003eNature\u003c/em\u003e 542 (2017): 86\u0026ndash;90. https://doi.org/10.1038/nature21048.\u003c/li\u003e\n\u003cli\u003eInstitut National de la Statistique (INS). \u003cem\u003eRapport Pr\u0026eacute;liminaire du Recensement G\u0026eacute;n\u0026eacute;ral de la Population et de l\u0026rsquo;Habitat.\u003c/em\u003e Brazzaville: INS, 2024. https://ins-congo.cg/rapport-preliminaire/.\u003c/li\u003e\n\u003cli\u003eTyukavina, Alexandra, et al. \u0026quot;Congo Basin Forest Loss Dominated by Increasing Smallholder Clearing.\u0026quot; \u003cem\u003eScience Advances\u003c/em\u003e 4 (2018): eaat2993. https://doi.org/10.1126/sciadv.aat2993.\u003c/li\u003e\n\u003cli\u003eKottek, M., J. Grieser, C. Beck, B. Rudolf, and F. Rubel. \u0026quot;World Map of the K\u0026ouml;ppen-Geiger Climate Classification Updated.\u0026quot; \u003cem\u003eMeteorologische Zeitschrift\u003c/em\u003e 15, no. 3 (2006): 259\u0026ndash;263. http://dx.doi.org/10.1127/0941-2948/2006/0130.\u003c/li\u003e\n\u003cli\u003eClimate Data Organization. \u0026quot;Climate Statistics for Brazzaville, Congo-Brazzaville.\u0026quot; Accessed 2024. https://fr.climate-data.org/afrique/congo-brazzaville/brazzaville/brazzaville-4600/.\u003c/li\u003e\n\u003cli\u003eTamoffo, Alain T., Alessandro Dosio, Torsten Weber, and Derbetini A. 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Zaitchik. \u0026quot;The Impact of Climate Change on Agriculture Production in Ethiopia: Application of a Dynamic Computable General Equilibrium Model.\u0026quot; \u003cem\u003eAmerican Journal of Climate Change\u003c/em\u003e 10, no. 1 (2021): 32\u0026ndash;50. https://doi.org/10.4236/ajcc.2021.101003.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Climate projections, SSP scenarios, Temperature variability, Precipitation trends, Brazzaville","lastPublishedDoi":"10.21203/rs.3.rs-6496514/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6496514/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change is a key driver of climatic instability and hydroclimatic extremes, particularly in regions like Brazzaville, in the Republic of Congo, where rising mean surface temperatures and increasing precipitation variability pose critical risks to water resources, agriculture, and urban infrastructure. Despite advances in global climate modelling (GCM), the lack of high-resolution and regional-specific projections limits data-driven adaptation strategies, hindering climate resilience planning. This study aims to assess future temperature and precipitation changes in Brazzaville using CMIP6 climate projections across different time scales: historical time (1981\u0026ndash;2010), mid-century time (2031\u0026ndash;2060), and late-century time (2071\u0026ndash;2100). The study employs climate modelling and statistical analysis, utilizing CMIP6 projections from the ACCESS-CM2 model under the SSP2-4.5 scenario. Climate data were processed using MATLAB, incorporating bias correction (quantile mapping), statistical downscaling (CORDEX methods), trend analysis, and correlation assessments. Key climate indicators, including temperature and precipitation, were evaluated using mean, median, and standard deviation. Results show a statistically significant warming trend, with mean temperatures projected to increase by 3.5\u0026deg;C (\u0026plusmn;\u0026thinsp;0.2\u0026deg;C) between 2071 and 2100 compared to the historical baseline. Monthly mean temperatures may exceed 32.1\u0026deg;C during peak heat events, with seasonal anomalies suggesting an increase in extreme heat events beyond the 95th percentile. Precipitation projections show a\u0026thinsp;+\u0026thinsp;6.8% increase in peak wet season rainfall, while dry season precipitation is projected to decline by \u0026minus;\u0026thinsp;10.3%, worsening seasonal contrasts. The probability of extreme precipitation events (\u0026gt;\u0026thinsp;90 mm/day) increases by 14.7%, showing a higher risk of flooding and runoff intensification. Meanwhile, consecutive dry days (CDDs) in the dry season are expected to increase by +\u0026thinsp;8 days per year, heightening drought severity and soil moisture deficits. Future microclimate is expected to undergo significant variability due to global climate change.\u003c/p\u003e","manuscriptTitle":"Projected Temperature and Precipitation Changes in Brazzaville, Republic of Congo: A CMIP6-Based Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 15:17:04","doi":"10.21203/rs.3.rs-6496514/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"47f35e3a-a15d-4ded-a9c1-46a368ba026f","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-04T14:23:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 15:17:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6496514","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6496514","identity":"rs-6496514","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}