Projected near-future changes in precipitation extremes over Anambra-Imo River Basin inferred from CMIP6 HighResMIP

Projected near-future changes in precipitation extremes over Anambra-Imo River Basin inferred from CMIP6 HighResMIP | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Projected near-future changes in precipitation extremes over Anambra-Imo River Basin inferred from CMIP6 HighResMIP Colman Ibe, Victor Nnamdi Dike, Samaila Kunden Ishaya, Jos Magaji, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4303083/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The southeastern region of Nigeria is susceptible to flood disasters primarily triggered by extreme precipitation with localized impacts. This study uses the Coupled Model Intercomparison Project Phase 6 (CMIP6), High-Resolution Model Intercomparison Project (HighResMIP) to investigate seasonal dependent changes in precipitation extremes in the near future (2031–2050) in the Anambra-Imo River Basin, in the southeastern region of Nigeria. Evaluating the models against observation for the 1995–2014 period, it is found that models creditably reproduced the spatial pattern of the observed annual precipitation extremes over the river basin. Results show that in the near future, annual precipitation extremes will be characterized by a robust increase in annual total precipitation amount (PRCPTOT), maximum 5-day precipitation (RX5day), and heavy precipitation (R10mm). Meanwhile, the models project a significant increase in PRCPTOT, RX5day, R10mm, and wet-day intensity (SDII) for the June-July-August (JJA) and September-October-November (SON) seasons. The results demonstrate a robust and higher magnitude increase in precipitation extremes during the SON season. Specifically, PRCPTOT, RX5day, R10mm and SDII are projected to increase by up to 46 mm, 24 mm, 1.2 days and 2.4 mm/day, respectively. Whereas during the March-April-May (MAM) season, the HighResMIP suggests that PRCPTOT, R10mm, and SDII will marginally increase over the eastern part of the Anambra-Imo River Basin. Besides, the December-January-February (DJF) season will be characterized by a marginal increase in the precipitation extremes, especially over the southern fringes of the river basin. We note that in the near future, precipitation extremes in the river basin will be characterized by more intense and less frequent precipitation extremes during the JJA and SON, potentially exacerbating flash flooding in the river basin. Hence, the results of this study may be vital for near-term socio-economic planning and policy decisions that will minimize the impact of flood disasters in the Anambra-Imo River Basin. Precipitation extremes CMIP6 HighResMIP Anambra-Imo River Basin Nigeria Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1.0 Introduction Precipitation is a key component of the hydrological cycle, which promotes socioeconomic activities and ecological stability in the Anambra-Imo River basin [ 1 , 2 ]. The river basin is located in Nigeria's southeastern area and is distinguished by the complexity of its hydrological system [ 3 ], climate change has aggravated the basin's vulnerability to severe precipitation extremes [ 4 , 5 ]. Meanwhile, precipitation extremes have maintained their capacity to exert a severe impact on Nigerian river basins in recent years [ 5 , 6 ]. Torrential precipitation has caused disastrous floods that induced significant damage to the river basin, resulting in massive economic losses [ 5 ].In this regard, the Nigerian Emergency Management Agency estimated that the 2012 flooding claimed over 400 individuals and triggered a 16 million US dollar damage to the economy, whereas the 2022 flood claimed over 600 people and impacted an estimated 3.2 million people [ 7 ]. Predicting the impact of potential extreme precipitation is critical for effective river basin management of water resources, infrastructure development, and climate adaptation measures [ 8 – 10 ]. With the increasing concern about the future impact of climate change and its effects on regional hydrology, it is critical to study the potential changes in precipitation extremes in the Anambra-Imo River Basin. In this context, the CMIP6-endorsed High-Resolution Model Intercomparison Program (HighResMIP; [ 11 ]) offers an effective tool for investigating the near-term precipitation changes using high-resolution climate projections [ 12 , 13 ]. The HighResMIP project attempts to enhance our grasp of climate anomalies through the simulation of more precise and reliable projections [ 14 ]. Given the incorporation of more detailed topography, land surface processes, and atmospheric dynamics in these models and their high-resolution structure, it presents a special tool for the prediction of regional-scale extreme precipitation events [ 11 , 15 ]. Several studies have reported the creditable performance of HighResMIP models in simulating mean precipitation and precipitation extremes in different regions across the world [ 15 – 19 ].However, understanding the potential near-future changes in precipitation extremes will provide the crucial insight required to assist policymakers in establishing essential regulations that will forestall the impact of climate change and its implications on the population [ 8 ]. Recent studies have examined the impact of climate change on precipitation extremes in Nigeria [ 20 – 23 ]. For example, Salihu et al., [ 22 ] investigated projected changes in precipitation extremes over Nigeria using CMIP5 models and found a substantial increase in maximum 5-day rainfall and heavy rainfall days under high emission scenarios. While Hassan et al., [ 20 ] established that mean precipitation will increase by about 2.62%, implying a potential spike in the dangers associated with flooding, particularly in southern Nigeria. Despite the fact that these studies indicate an increase in precipitation in the river basin, Shiru et al., [ 23 ] found that drought will be frequent in most parts of the country. At the river basin scale, a consensus on the changes in precipitation extremes has not been reached based on a multi-model ensemble of high-resolution models. Therefore, this study focuses on the multi-model projection of near-term seasonal dependent changes in precipitation extremes in the Anambra-Imo River Basin. The primary objective of this study is to examine the projected near-future changes in extreme precipitation events using an ensemble of high-resolution models that participated in the HighResMIP project. This study will provide a comprehensive assessment of the uncertainties associated with future precipitation extremes over the Anambra-Imo River Basin. The multi-model approach offers insights into the range of possible outcomes, improving our understanding of the robustness and reliability of the projected changes. The rest of the study is presented as follows, the observation data, CMIP6-endorsed HighResMIP model data, and methods are discussed in section 2, and the results and discussions are summarised in section 3. Finally, section 4 highlights our major conclusions. 2.0 Study area, data, and methods 2.1 Study Area Anambra-Imo River basin is located in southeastern Nigeria, within the geographic coordinate of Latitude 5°-7°N and Longitude 6.5° −8.5°E. Figure 1 . The river basin is comprised of five States namely Anambra, Abia, Ebonyi, Enugu, and Imo states, with an estimated area of about 28,658.8 km 2 . The tropical humid climate of southern Nigeria is regulated by the West African monsoon circulation, which supplies moisture to the region during the summer months [ 24 , 25 ]. Annual precipitation in the river basin is characterized by a bimodal precipitation pattern due to the reversal of the monsoon front [ 24 , 25 ]. The rainy season starts in March–April with the first peak in June–July and a second peak in September [ 6 , 26 – 29 ]. In the region, annual total precipitation reaches 2800 mm [ 1 , 5 ], with surplus rains during the rainy season. In the region, temperature and humidity remain relatively constant throughout the year in southern Nigeria, and the highest temperatures occur during the pre-monsoon season. Socio-economic activities are striving in the region, with massive infrastructural development for the growing population. However, the landscape of the river basin makes it vulnerable to flood disasters [ 5 , 30 ] induced by torrential precipitation [ 6 ]. 2.2 Data and Method 2.2.1 Observational and CMIP6 HighResMIP data Daily precipitation outputs from fifteen High-Resolution Model Intercomparison Project (HighResMIP; [ 11 ]) models were used in this study. The dataset consists of outputs of AMIP-style experiments, namely; highresSST-present and highresSST-future under the CMIP6 model experiment [ 31 ], designed to promote high-resolution simulations. The historical simulations run from 1950 to 2014, whereas the future climate simulations run from 2015 to 2050 period. Notably, near-future simulations are forced by high emission Shared Socioeconomic Pathway (SSP5-8.5; [ 32 ]). In this study, we set the baseline to cover the 1995–2014 historical period and 2031–2050 for the near-future period. The performance of the models was evaluated against an ensemble of two high-resolution daily gridded precipitation products from the Climate Hazard Group InfraRed Precipitation (CHIRPS version 2; [ 33 ]) and daily precipitation from Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset version 2 [ 34 ]. The observational datasets are produced at a higher spatial resolution of 0.05º x 0.05º and 0.1º x 0.1º for CHIRPS and MSWEP datasets, respectively. These observational datasets are acclaimed for their robustness in representing precipitation over West Africa [ 35 – 37 ]. Both observational data and model outputs are aggregated into the same resolution (0.25° x 0.25°) for easy comparison. An ensemble of both the CHIRPS and MSWEP datasets was constructed hereafter OBS and used as reference data in this study. Table 1 Basic description of the 15 CMIP6 HighResMIP models used in this study S/N Modeling organizations Model Name Atmospheric resolution References for HighResMIP models 1 Euro-Mediterranean Center on Climate Change (Italy) CMCC-CM2-HR4 1.00° × 1.00° [ 38 ] 2 CMCC-CM2-VHR4 0.25° × 0.25° 3 EC-EARTH consortium, (Sweden) EC-Earth3P-HR 0.35° × 0.35° [ 20 ] 4 Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), China FGOALS-f3-H 0.25° × 0.25° [ 39 ] 5 FGOALS-f3-L 1.00° × 1.25° 6 Geophysical Fluid Dynamics Laboratory/ NOAA (U.S.) GFDL-CM4C192 0.50° × 0.50° [ 40 ] 7 The UK Met Office Hadley Centre for Climate Change (UK) HadGEM3-GC31-MM 0.23° × 0.35° [ 41 ] 8 Geophysical Fluid Dynamics Laboratory/ NOAA (U.S.) HiRAM-SIT-HR 0.25° × 0.25° [ 42 ] 9 HiRAM-SIT-LR 0.50° × 0.50° 10 Max Planck Institute MPI, (Germany) MPI-ESM1-2-HR 0.94° × 0.94° [ 43 ] 11 MPI-ESM1-2-XR 0.47° × 0.47° 12 Meteorological Research Institute (Japan) MRI-AGCM3-2-H 0.56° × 0.56° [ 44 ] 13 MRI-AGCM3-2-S 0.19° × 0.19° [ 45 ] 14 Japan Agency for Marine-Earth Science and Technol Ogy (Japan) NICAM16-7S 0.56° × 0.56° [ 46 ] 15 NICAM16-8S 0.28° × 0.28° 2.2.2 Extreme precipitation indices and model validation metrics This study uses extreme climate indices described by the Expert Team on Climate Change Detection and Indices (ETCCDI; [ 47 ]) to investigate future changes in precipitation extremes in the River Basin. The extreme precipitation indices are listed in Table 2 and have been widely applied to study changes in future precipitation extremes around the globe [ 6 , 8 , 9 , 48 – 51 ] In particular, this study uses the indices that mainly represent the intensity and frequency of extreme precipitation. These indices are employed herein to study the seasonally dependent near-future changes in precipitation extremes over the Anambra-Imo River Basin. Table 2 Description and unit of extreme precipitation indices analyzed ID Name Description Units PRCPTOT Total wet-day precipitation amount Total precipitation in wet days (P ≥ 1 mm), defined as \({P}_{ij}\) representing daily precipitation amount on day i in a period j . If the i denote the number of days in j , then; \({PRCPTOT}_{j}=\sum _{i=1}^{i}{P}_{ij}\) mm RX5day Maximum consecutive 5-day precipitation Maximum rainfall sum for 5-day interval. Let \({P}_{ij}\) be the precipitation amount for the 5-day interval i ending in period j . The maximum 5-day values are estimated for the period as: \({RX5day}_{j}\) = max( \({P}_{ij}\) ) mm R10mm Heavy precipitation days Number of very heavy precipitation days (P ≥ 10mm). That is; let \({P}_{ij}\) be the daily precipitation amount where \({P}_{ij}\ge 10 mm\) days SDII Wet-day intensity Average precipitation from wet-days. This can be defined as \({P}_{wj}\) be the daily precipitation amount on wet days, w (P ≥ 1 mm) in period j . If w represents number of wet days in j , then \({SDII}_{j}=\frac{\sum _{w=1}^{w}{P}_{wj}}{W}\) mm/day CWD wet spells Maximum number of consecutive dry days (P ≥ 1 mm). Let \({P}_{ij}\) be the daily precipitation amount on day i in period j . Count the largest consecutive days where \({P}_{ij}\ge 1mm\) days CDD dry spells Maximum number of consecutive dry days (P ≤ 1 mm). Let \({P}_{ij}\) be the daily precipitation amount on day i in period j . Count the largest consecutive days where \({P}_{ij}\le 1mm\) days Furthermore, we used statistical evaluation metrics such as mean bias (MB), the normalized root mean square error (NRMSE) and the Pattern correlation coefficient (PCC). The mean bias for each model is calculated with respect to the observations as follows; \(bias=\frac{\left(\overline{x}-\overline{y}\right)}{\text{y}}\) where x and y denote the map of each CMIP6 HighResMIP model and the observed field, respectively, and the over-bars ( – ) indicate their means. The MB provides information on the difference in magnitude between observation and models. The PCC between the model and observation is defined as follows. $$PCC= \frac{1}{{\phi }_{x}{\phi }_{y}}\left[\frac{1}{N}\sum _{i=1}^{N}\left({x}_{i}-\stackrel{-}{x}\right)({y}_{i}-\stackrel{-}{y})\right]$$ Sillmann et al., [ 52 ] used the normalized root-mean-square error (NRMSE), among other metrics to evaluate the performance of climate models. The NRMSE is also computed for the extreme precipitation indices following the same approach. $$NRMSE= \frac{\sqrt{\frac{1}{N}\sum _{i=1}^{N}{({x}_{i}-{y}_{i})}^{2}}}{\frac{1}{N}\sum _{i=1}^{N}{y}_{i}}$$ where N is the number of grid cells, x i and y i are the variable values to the ith grid cell of the models and the observed field, respectively. Whereas the \(\overline{x}\) and \(\overline{y}\) their mean values, φ x and φ y their standard deviations. Both NRMSE and PCC provide information on the model performance. 2.2.3 Analysis of the projected future changes The projected near-future changes in precipitation extremes are quantified by the relative change between the projection (2031–2050) period and the baseline period (1995–2014). The future projections are considered for four seasons to obtain seasonal-dependent changes in precipitation extremes for the river basin. Moreover, we consider the projected changes robust when the ratio of the mean to the standard deviation of the climate change signals is equal to or larger than one. As such, when at least 70% of the models agree to defined metric, we consider the sign of the projected change in the precipitation extremes robust. We determined statistically significant projected change based on student's t test at 95% confidence level. 3.0 Results and Discussions 3.1 Performance of the CMIP6 HighResMIP models in representing observed precipitation extremes Figure 2 illustrates the spatial distribution of observed annual mean precipitation extremes, multi-model ensemble mean (MME) of HighResMIP models as well as their mean bias relative to the observation. The observation shows a high and low magnitude of annual total precipitation (PRCPTOT) over the lower reaches and upper part of the Anambra-Imo River basin, respectively, during the 1995–2014 period. Similarly, annual heavy precipitation days (R10mm), maximum 5-day precipitation (RX5day), wet-day intensity (SDII), and wet spells (CWD) follow the same pattern in the river basin (Fig. 2 a-e). Conversely, it is found that the dry spell is more prevalent over the part of the river basin encompassing the northern extremities of Anambra, Enugu, and Ebonyi states (Fig. 2 f ) . Interestingly, the MME reproduced the observed annual precipitation extremes ( Fig. 2 g-l ) , albeit indications that the models overestimated the magnitude of the observed annual precipitation extremes. The models overestimated RX5day, PRCPTOT, R10mm, CWD, SDII, and CDD (Fig. m-r) . Specifically, it is found that the models overestimated PRCPTOT, R10mm, and SDII by 60%, 10%, and 40%, respectively, whereas the magnitude of the mean bias is about 80% for CWD, RX5day, and CDD during the 1995–2014 period. The foregoing indicates that biases known to exist in GCMs also persist in the HighResMIP simulation of precipitation extremes. The biases in the HighResMIP simulated precipitation extremes are perhaps related to the resolution of the models. Although the HighResMIP is acclaimed for its high resolution, nearly half of the models used in this study have a grid spacing larger than 0.5°, affecting the model's performance in the river basin. Furthermore, we examine the performance of the models by considering the pattern correlation coefficients (PCC) and normalized root mean square errors (NRMSE) for the 35 grid cells within the river basin ( Fig. 3 a-b ) . Results show that the models creditably reproduced the spatial pattern of the annual precipitation extremes with PCC > 0.8 for MME. Particularly, FGOALS-f3-L, CMCC-CM2-HR4, HadGEM3-GC31-MM, MPI-ESM1-2-XR, NICAM16-7S, and NICAM16-8S performed better than other models with PCC > 0.8 for all the extreme precipitation indices. Meanwhile, some of the models show lesser performance in reproducing the spatial pattern of SDII over the river basin, with PCC ≤ 0.4. It is also found that the models simulated the climatologies of annual mean precipitation extremes with minimum errors by CMCC-CM2-HR4, GFDL-CM4C192, HadGEM3-GC31-MM, HiRAM-SIT-HR, MPI-ESM1-2-XR, MPI-ESM1-2-HR, and NICAM16-7S (Fig. 3 b). Meanwhile, the NRMSEs are consistently large for CWD and CDD, this is perhaps linked to the inability of the models in simulating the number of rainy days [ 51 ] and resolving the complex terrain over the Guinea coast [ 53 ]. Generally, the models captured the observed features of annual mean precipitation extremes over the Anambra-Imo river basin during the 1995–2014 period. 3.2 Annual and seasonal dependent changes in precipitation extremes Figure 4 depicts the spatial pattern of the projected changes in annual mean precipitation extremes during 2031–2050 compared to 1995–2014. The multi-model projection of near-term precipitation extremes indicates an increase in annual total precipitation (PRCPTOT), maximum 5-day precipitation (RX5day), and heavy precipitation days (R10mm); the results further indicate that the high-resolution models project shows a robust increase in extreme precipitation (Fig. 4 a-c). Meanwhile, the HighResMIP models project no increase in annual wet-day intensity (SDII), whereas a non-significant increase is projected for wet spells over Enugu, Anambra, Ebonyi, and Abia states (Fig. 4 d-e). Besides, Fig. 4 f shows a non-significant decrease in dry spells over the river basin. The foregoing indicates that in the near-future, the annual increase in PRCPTOT is mostly associated with the projected increase in precipitation intensity rather than its frequency. The projected increase in RX5day is consistent with the results obtained by Salihu et al., [ 22 ] for the Guinea Coast region of Nigeria based on CMIP5 models. Besides, Salihu et al., [ 22 ] reported a significant increase in RX5day during the present-day period; this suggests that future increases in the precipitation extremes will trigger more disaster flood disasters in the region. Diba et al., [ 54 ] reported that the intensification of precipitation over West Africa is related to the warm extremes. According to the Clausius–Clapeyron equation, global warming has accelerated the local water cycle and increased advected moisture, leading to the intensification in precipitation extremes [ 55 ]. Next, we discuss the projected changes in precipitation extremes at seasonal timescales. Figure 5 shows the projected changes in March-April-May (MAM) precipitation extremes over the Anambra-Imo River basin. Figure 5 a shows that the western parts of the river basin, specifically Anambra and Imo states, will experience a non-significant decrease in PRCPTOT during the spring season while other parts of the river basin will record a non-significant increase in PRCPTOT. A similar pattern will be recorded for RX5day, R10mm, SDII, and CWD (Fig. 5 a-e). However, it should be noted that most parts of Enugu, Ebonyi, and Abia states will experience an increase in precipitation intensity (Fig. 5 c-d), while a dry spell will increase during the MAM season (Fig. 5 f). Interestingly, the models project a robust and statistically significant increase in summer (JJA) precipitation extremes over the river basin (Fig. 6 a-d). The increase in summer PRCPTOT is mostly linked to intensity-related extremes viz RX5day and SDII, especially over Enugu, Ebonyi, and Abia states. Although the projected increase in R10mm and CWD is not statistically significant, the decrease in CDD suggests that the river basin will experience an increase in intensity and less frequent summer precipitation extremes. This is consistent with the findings by Dike, Lin and Ibe [ 6 ] for the sub-region in recent decades. Furthermore, the spatial distribution of the projected changes in September-October-November shows that in the near-future, the river basin will experience a greater magnitude of increase in precipitation extremes (Fig. 7 ). The models project a wide-spread, robust, and significant increase in PRCPTOT, RX5day, R10mm, and SDII (Fig. 7 a-d). Essentially, the state in the northern fringes of the river basin (Anambra, Enugu, and Ebonyi) will record a notable increase in the precipitation extremes. The precipitation is projected to increase by up to 46mm, 1.2 days, 24mm, and 2.4mm/day for PRCPTOT, R10mm, RX5day, and SDII, respectively. It is noteworthy to state that the projected changes are robust as more than 70% of the models herein considered agree with the projected significant increase in SON precipitation extremes over the river basin. This increases the vulnerability of the river basin to flood disasters in the future [ 7 ]. Meanwhile, the projected change in CWD shows a non-homogenous increase (decrease) in wet spells, whereas the projected decrease in CDD is widespread (Fig. 7 e-f). As with the findings for the summer season, the projected increase in the extremes is also linked to the increase in the intensity and less frequent precipitation extremes [ 6 , 51 , 56 ]. Both the JJA and SON seasons will experience a significant and severe increase in precipitation extremes, with higher magnitude recorded in the SON season. Specifically, Hassan et al., [ 20 ] used the downscaled CMIP5 model to demonstrate that mean precipitation will increase significantly in July-August-September in most parts of the Niger-Delta region. This indicates that water vapour will be more abundant during the July-August-September season in the near future. Meanwhile, previous studies have illustrated that future precipitation in the region will be characterized by intense precipitation extremes and highlighted the propensity of later wet seasons with more intense precipitation under future climate change [ 35 , 57 ]. Finally, Fig. 8 shows that during the December-January-February (DJF) season, PRCPTOT, RX5day, R10mm, and CDD will increase slightly. Specifically, Fig. 8 a-c shows that winter PRCPTOT, RX5day, and R10mm will increase in most parts of the river basin. No notable change is projected for SDII for the DJF season over the river basin (Fig. 8 d). However, RX5day will increase significantly by 10mm over most parts of Imo state (Fig. 8 b), while CWD will increase (decrease) by 1 day relative to the present-day levels in the southern (northern) parts of the river basin. Remarkably, CDD will increase slightly in the river basin as well, suggesting that both wet and dry precipitation extremes will increase in the future during the DJF season (Fig. 8 a-f). More so, the increase in the precipitation extremes is projected to extend to the DJF season, which indicates a wet winter season in the river basin under SSP5-8.5 emission scenario. Studies have indicated the changes in precipitation seasonality over the region, especially under high-emission scenarios [ 35 , 57 , 58 ]. Meanwhile, the projected increase in the seasonal precipitation extremes, especially during the JJA, SON, and DJF seasons, contributes significantly to the projected annual precipitation extremes in the near future. The significant increase in the intensity-related precipitation extremes implies a potential risk of intensified extreme precipitation and the associated flood disasters [ 5 , 8 ], which would increase the vulnerability of key socioeconomic sectors, such as agriculture and water management under SSP5-8.5 emission scenario [ 12 , 49 , 59 ]. 4.0 Conclusion This study investigates the projected future changes in seasonal precipitation extremes over Anambra-Imo River basin in southeastern Nigeria. Firstly, fifteen AMIP-type historical simulations of the CMIP6 HighResMIP were evaluated against an ensemble of two gridded observation products to assess the ability of the models to reproduce the observed precipitation extremes. The results show that the models creditably reproduced the spatial distribution of the observed precipitation extremes. Specifically, CMCC-CM2-HR4, FGOALS-f3-L, HadGEM3-GC31-MM, MPI-ESM1-2-XR, NICAM16-7S, and NICAM16-8S performed better than other models with PCC > 0.8 for all the extreme precipitation indices. Consistent with the results obtained by Ajibola et al., [ 17 ] for the entire West Africa region. Although the models seemingly overestimated the precipitation extremes, it is also found that the models simulated the climatologies of annual mean precipitation extremes with minimum errors. Specifically, CMCC-CM2-HR4, GFDL-CM4C192, HadGEM3-GC31-MM, HiRAM-SIT-HR, MPI-ESM1-2-XR, MPI-ESM1-2-HR, and NICAM16-7S simulated the precipitation extreme with minimal errors. However, the NRMSEs are consistently large for CWD and CDD; this is perhaps linked to the inability of the models to simulate the number of rainy days over the river basin. Furthermore, the HighResMIP models project a significant increase in annual total wet-day precipitation (PRCPTOT) for 2031–2050 relative to the 1995–2014 period under the SSP5-8.5 emission scenario. The projected increase in PRCPTOT is mostly linked to the increase in maximum 5-day precipitation (RX5day) and heavy precipitation (R10mm). Consistent with the projected increase in these precipitation extremes indices, the future total annual dry spells will decrease in the river basin. It was also found that during the MAM season, most parts of Enugu, Ebonyi, and Abia will experience an increase in R10mm and SDII, leading to an increase in MAM PRCPTOT in these aforementioned areas. However, it should be noted that the projected increase in these extreme precipitation indices is not statistically significant and has notable uncertainty. Meanwhile, the models project an increase in dry spells during the MAM season, which indicates a drier spring over the river basin in the near-future. Nonetheless, the HighResMIP models project a notable increase in summer precipitation extremes. Results further demonstrate that summer PRCPTOT, R10mm, RX5day, and SDII will increase in most parts of the river basin, especially over the northern reaches of the Anambra-Imo River Basin encompassing Enugu, Ebonyi and Abia state. The spatial distribution of consecutive wet days shows that wet spells will also increase over these areas, whereas dry spells will decrease. Relatedly, precipitation extremes during the September-October-November (SON) will be characterized by a similar pattern projected for the summer season but with a higher magnitude. This is an indication that the river basin will experience an increase in intensity and less frequent summer precipitation extremes in the near future, especially during the SON season. Similar results have also been obtained for other river basins worldwide [ 9 , 12 , 13 ]. We note that in the near future, the SON will characterized by a higher magnitude increase in precipitation extremes, which will potentially exacerbate flash flooding in the river basin during the season. We highlight the need for a detailed analysis of the potential physical mechanisms driving the projected changes in the precipitation extremes in the river basin. Meanwhile, for the December-January-February (DJF) season, the models project a marginal increase in winter precipitation extremes like RX5day and R10mm, with a higher magnitude of RX5day over the Imo state. This suggests that the fundamentally dry season will be wetter in the near future. Additionally, the river basin will experience more intense precipitation during the JJA and SON seasons, contributing significantly to the projected increase in annual total precipitation in the river basin. It is also important to note that in the near future, precipitation extremes in the river basin will be characterized by more intense and less frequent precipitation extremes, potentially exacerbating flash flooding in the vulnerable river basin [ 3 ]. Meanwhile, we presented the future changes in precipitation extremes in the river basin without analyzing the mechanism associated with these projected changes. Therefore, further analysis is needed to identify the circulation features associated with these projected changes. Besides, one notable limitation of this study is the horizontal resolution of some of the HighResMIP models used in this study. The spatial resolutions of the models are still coarse, considering the size of the river basin. Therefore, further studies over the river basin should consider higher resolution regionally downscaled models. Despite these limitations, the inferences drawn from this study will provide a vital resource needed for near-term socio-economic planning and adaptation measures that will minimize the impact of flood disasters in the Anambra-Imo River Basin. Declarations Declaration of Competing Interest No conflict of interest. All the authors agree with the content of this study. Author Contribution ICC: Conceptualization; data curation; formal analysis; writing – original draft; writing – review and editing. DVN: Conceptualization; data curation; formal analysis; funding acquisition; methodology; writing – original draft; writing – review and editing. SKI and MJI: Supervision; writing – original draft; writing – review and editing. IAA and ACM: Writing – review and editing. Acknowledgement The authors thankfully acknowledge the funding from the NSFC research fund for international young scientists (Grant No. 42150410394) and the support from the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (EarthLab). Data Availability The data that support the findings of this study are openly available at the following sources: HighResMIP: https://esgf-node.llnl.gov/projects/cmip6/ CHRIPS: http://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/ MSWEP: https://www.gloh2o.org/mswep/ References Ogungbenro, S.B.; Morakinyo, T.E. Rainfall distribution and change detection across climatic zones in Nigeria. Weather and Climate Extremes 2014, 5–6 , 1–6, doi: https://doi.org/10.1016/j.wace.2014.10.002 . Ogunrinde, A.T.; Oguntunde, P.G.; Akinwumiju, A.S.; Fasinmirin, J.T. Analysis of recent changes in rainfall and drought indices in Nigeria, 1981–2015. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4303083","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":300210816,"identity":"970ed167-9f47-4093-b9cf-dcc358a84ab4","order_by":0,"name":"Colman Ibe","email":"","orcid":"","institution":"Imo State Polytechnic Omuma","correspondingAuthor":false,"prefix":"","firstName":"Colman","middleName":"","lastName":"Ibe","suffix":""},{"id":300210819,"identity":"a2f1f4de-7a9a-4552-a468-ec183c0d51ad","order_by":1,"name":"Victor Nnamdi Dike","email":"","orcid":"","institution":"Imo State Polytechnic Omuma","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"Nnamdi","lastName":"Dike","suffix":""},{"id":300210822,"identity":"ef98b6d8-4e16-4475-a0e8-acb888cf36a4","order_by":2,"name":"Samaila Kunden Ishaya","email":"","orcid":"","institution":"Nasarawa State University Keffi","correspondingAuthor":false,"prefix":"","firstName":"Samaila","middleName":"Kunden","lastName":"Ishaya","suffix":""},{"id":300210824,"identity":"41df2f4e-84ce-4953-b76d-a56984c41743","order_by":3,"name":"Jos Magaji","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYLACCQM2Bgb2BiDLwIIULTwHQFokSLIqAUwSVqjbfvziB4sCPjn5mc+vbvhRIMHA396dgFeL2ZmcYgmgw4wNbueU3ewBOkzizNkN+LUcyEkAaUncIJ2TdoMHqMVAIpeAlvNvkn+AtMyfeSbt5h+itNxIPwa2peEG+7HbxNly4w2bBdgvZ3LYbssYSPAQ9sv59Me3Jf4ck5NvP/7s5ps/NnL87b34tQCj0IBZguEYmAHmElAOAuwPGD8w1IAZRKgeBaNgFIyCkQgALQlHCUimBgYAAAAASUVORK5CYII=","orcid":"","institution":"Nasarawa State University Keffi","correspondingAuthor":true,"prefix":"","firstName":"Jos","middleName":"","lastName":"Magaji","suffix":""},{"id":300210826,"identity":"7c758be5-be23-47d5-afa2-32a9bedd1133","order_by":4,"name":"Amarachukwu A. 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The black lines show the geographical locations of the five main sub-regions (Anambra, Abia, Imo, Enugu, and Ebonyi States) in the river basin. The topography is coloured based on a digital elevation map; the green colour indicates the low-level areas, while the red colour indicates higher elevation.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4303083/v1/0bae27b3e57f05410b4c871f.png"},{"id":56144382,"identity":"39bfc7ed-b003-4433-bbfa-914e5337fdda","added_by":"auto","created_at":"2024-05-09 05:14:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":288803,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial pattern of Annual (ANN) precipitation extremes, PRCPTOT, RX5day, R10mm, SDII, CWD, and CDD. (a-f) ensemble of both CHIRPS and MSWEP, observation (OBS), (g-l) Multi-model Ensemble Mean (MME), and (m-r) percentage bias between the MME \u0026nbsp;of HighResMIP models relative to the OBS for the present-day (1995-2014) period.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4303083/v1/640ed6dfcc92f2375e0b9294.png"},{"id":56144938,"identity":"86f11d50-2ea2-42d0-a6c4-aecc291ecbf2","added_by":"auto","created_at":"2024-05-09 05:23:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":78484,"visible":true,"origin":"","legend":"\u003cp\u003ePortrait diagram of spatially averaged of mean annual precipitation extremes relative to observation for the present-day (1995-2014) perioda) pattern correlation and b) normalized root mean square errors.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4303083/v1/9fbcb84e3424647b65013999.png"},{"id":56144567,"identity":"60d07a56-f6f7-437e-8e81-c24bca18acaa","added_by":"auto","created_at":"2024-05-09 05:15:52","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":311990,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial pattern of the projected changes in Annual (ANN) precipitation extremes based on the SSP5-8.5 scenario for the near future (2031-2050) relative to the 1995–2014 period. The black stippling denotes statistically significant changes at the 95% confidence level, while areas with slanted green boxes indicate where at least 70% of models agree with the sign of projected future changes.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4303083/v1/50b18adc285d794802d78cdd.jpeg"},{"id":56144566,"identity":"ed978546-66d9-4f32-89d6-581648ff357b","added_by":"auto","created_at":"2024-05-09 05:15:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":231844,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial pattern of the projected changes in March-April-May (MAM) precipitation extremes based on the SSP5-8.5 scenario for the near future (2031-2050) relative to the 1995–2014 period. The black stippling denotes statistically significant changes at the 95% confidence level, while areas with slanted green boxes indicate where at least 70% of models agree with the sign of projected future changes.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4303083/v1/df4b209c21f07087d56e0361.png"},{"id":56144578,"identity":"509b8d90-ad38-4a56-ba86-cbec3dd404f8","added_by":"auto","created_at":"2024-05-09 05:15:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":314492,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial pattern of the projected changes in June-July-August (JJA) precipitation extremes based on the SSP5-8.5 scenario for the near future (2031-2050) relative to the 1995–2014 period. The black stippling denotes statistically significant changes at the 95% confidence level, while areas with slanted green boxes indicate where at least 70% of models agree with the sign of projected future changes.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4303083/v1/9dec1fe199f72b5b219cd77a.png"},{"id":56144515,"identity":"27973990-2ee6-4f82-acae-f22bc865e601","added_by":"auto","created_at":"2024-05-09 05:15:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":357454,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial pattern of the projected changes in September-October-November (SON) precipitation extremes based on the SSP5-8.5 scenario for the near future (2031-2050) relative to the 1995–2014 period. The black stippling denotes statistically significant changes at the 95% confidence level, while areas with slanted green boxes indicate where at least 70% of models agree with the sign of projected future changes.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4303083/v1/6f7d3d4e0df80a2d659dd7fd.png"},{"id":56144496,"identity":"ee145cd9-a8fc-4d86-b023-26da7e479096","added_by":"auto","created_at":"2024-05-09 05:15:13","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":194939,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial pattern of the projected changes in December-January-February (DJF) precipitation extremes based on the SSP5-8.5 scenario for the near future (2031-2050) relative to the 1995–2014 period. The black stippling denotes statistically significant changes at the 95% confidence level, while areas with slanted green boxes indicate where at least 70% of models agree with the sign of projected future changes.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4303083/v1/38e9624a2980ee7a3d0c3836.png"},{"id":56144983,"identity":"20edfafc-5580-48a7-a2f9-b49b7e13258a","added_by":"auto","created_at":"2024-05-09 05:24:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2412489,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4303083/v1/b5ddd96e-502d-4383-aaa9-3077b37d9f80.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Projected near-future changes in precipitation extremes over Anambra-Imo River Basin inferred from CMIP6 HighResMIP","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003ePrecipitation is a key component of the hydrological cycle, which promotes socioeconomic activities and ecological stability in the Anambra-Imo River basin [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The river basin is located in Nigeria's southeastern area and is distinguished by the complexity of its hydrological system [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], climate change has aggravated the basin's vulnerability to severe precipitation extremes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Meanwhile, precipitation extremes have maintained their capacity to exert a severe impact on Nigerian river basins in recent years [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Torrential precipitation has caused disastrous floods that induced significant damage to the river basin, resulting in massive economic losses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].In this regard, the Nigerian Emergency Management Agency estimated that the 2012 flooding claimed over 400 individuals and triggered a 16\u0026nbsp;million US dollar damage to the economy, whereas the 2022 flood claimed over 600 people and impacted an estimated 3.2\u0026nbsp;million people [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Predicting the impact of potential extreme precipitation is critical for effective river basin management of water resources, infrastructure development, and climate adaptation measures [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWith the increasing concern about the future impact of climate change and its effects on regional hydrology, it is critical to study the potential changes in precipitation extremes in the Anambra-Imo River Basin. In this context, the CMIP6-endorsed High-Resolution Model Intercomparison Program (HighResMIP; [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]) offers an effective tool for investigating the near-term precipitation changes using high-resolution climate projections [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The HighResMIP project attempts to enhance our grasp of climate anomalies through the simulation of more precise and reliable projections [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Given the incorporation of more detailed topography, land surface processes, and atmospheric dynamics in these models and their high-resolution structure, it presents a special tool for the prediction of regional-scale extreme precipitation events [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have reported the creditable performance of HighResMIP models in simulating mean precipitation and precipitation extremes in different regions across the world [\u003cspan additionalcitationids=\"CR16 CR17 CR18\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].However, understanding the potential near-future changes in precipitation extremes will provide the crucial insight required to assist policymakers in establishing essential regulations that will forestall the impact of climate change and its implications on the population [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recent studies have examined the impact of climate change on precipitation extremes in Nigeria [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For example, Salihu et al., [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] investigated projected changes in precipitation extremes over Nigeria using CMIP5 models and found a substantial increase in maximum 5-day rainfall and heavy rainfall days under high emission scenarios. While Hassan et al., [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] established that mean precipitation will increase by about 2.62%, implying a potential spike in the dangers associated with flooding, particularly in southern Nigeria. Despite the fact that these studies indicate an increase in precipitation in the river basin, Shiru et al., [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] found that drought will be frequent in most parts of the country. At the river basin scale, a consensus on the changes in precipitation extremes has not been reached based on a multi-model ensemble of high-resolution models. Therefore, this study focuses on the multi-model projection of near-term seasonal dependent changes in precipitation extremes in the Anambra-Imo River Basin.\u003c/p\u003e \u003cp\u003eThe primary objective of this study is to examine the projected near-future changes in extreme precipitation events using an ensemble of high-resolution models that participated in the HighResMIP project. This study will provide a comprehensive assessment of the uncertainties associated with future precipitation extremes over the Anambra-Imo River Basin. The multi-model approach offers insights into the range of possible outcomes, improving our understanding of the robustness and reliability of the projected changes. The rest of the study is presented as follows, the observation data, CMIP6-endorsed HighResMIP model data, and methods are discussed in section 2, and the results and discussions are summarised in section 3. Finally, section 4 highlights our major conclusions.\u003c/p\u003e"},{"header":"2.0 Study area, data, and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Study Area\u003c/h2\u003e\n\u003cp\u003eAnambra-Imo River basin is located in southeastern Nigeria, within the geographic coordinate of Latitude 5\u0026deg;-7\u0026deg;N and Longitude 6.5\u0026deg; \u0026minus;8.5\u0026deg;E. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The river basin is comprised of five States namely Anambra, Abia, Ebonyi, Enugu, and Imo states, with an estimated area of about 28,658.8 km\u003csup\u003e2\u003c/sup\u003e. The tropical humid climate of southern Nigeria is regulated by the West African monsoon circulation, which supplies moisture to the region during the summer months [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. Annual precipitation in the river basin is characterized by a bimodal precipitation pattern due to the reversal of the monsoon front [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. The rainy season starts in March\u0026ndash;April with the first peak in June\u0026ndash;July and a second peak in September [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. In the region, annual total precipitation reaches 2800 mm [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e], with surplus rains during the rainy season.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the region, temperature and humidity remain relatively constant throughout the year in southern Nigeria, and the highest temperatures occur during the pre-monsoon season. Socio-economic activities are striving in the region, with massive infrastructural development for the growing population. However, the landscape of the river basin makes it vulnerable to flood disasters [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e] induced by torrential precipitation [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Data and Method\u003c/h2\u003e\n\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.1 Observational and CMIP6 HighResMIP data\u003c/h2\u003e\n\u003cp\u003eDaily precipitation outputs from fifteen High-Resolution Model Intercomparison Project (HighResMIP; [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]) models were used in this study. The dataset consists of outputs of AMIP-style experiments, namely; highresSST-present and highresSST-future under the CMIP6 model experiment [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e], designed to promote high-resolution simulations. The historical simulations run from 1950 to 2014, whereas the future climate simulations run from 2015 to 2050 period. Notably, near-future simulations are forced by high emission Shared Socioeconomic Pathway (SSP5-8.5; [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]). In this study, we set the baseline to cover the 1995\u0026ndash;2014 historical period and 2031\u0026ndash;2050 for the near-future period. The performance of the models was evaluated against an ensemble of two high-resolution daily gridded precipitation products from the Climate Hazard Group InfraRed Precipitation (CHIRPS version 2; [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]) and daily precipitation from Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset version 2 [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. The observational datasets are produced at a higher spatial resolution of 0.05\u0026ordm; x 0.05\u0026ordm; and 0.1\u0026ordm; x 0.1\u0026ordm; for CHIRPS and MSWEP datasets, respectively. These observational datasets are acclaimed for their robustness in representing precipitation over West Africa [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. Both observational data and model outputs are aggregated into the same resolution (0.25\u0026deg; x 0.25\u0026deg;) for easy comparison. An ensemble of both the CHIRPS and MSWEP datasets was constructed hereafter OBS and used as reference data in this study.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBasic description of the 15 CMIP6 HighResMIP models used in this study\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eS/N\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eModeling organizations\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eModel Name\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAtmospheric resolution\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eReferences for HighResMIP models\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eEuro-Mediterranean Center on Climate Change (Italy)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eCMCC-CM2-HR4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e1.00\u0026deg;\u0026nbsp;\u0026times;\u0026nbsp;1.00\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eCMCC-CM2-VHR4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.25\u0026deg;\u0026nbsp;\u0026times;\u0026nbsp;0.25\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eEC-EARTH consortium, (Sweden)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eEC-Earth3P-HR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.35\u0026deg;\u0026nbsp;\u0026times;\u0026nbsp;0.35\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eInstitute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), China\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eFGOALS-f3-H\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.25\u0026deg;\u0026nbsp;\u0026times;\u0026nbsp;0.25\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eFGOALS-f3-L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e1.00\u0026deg;\u0026nbsp;\u0026times;\u0026nbsp;1.25\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGeophysical Fluid Dynamics Laboratory/ NOAA (U.S.)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGFDL-CM4C192\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.50\u0026deg;\u0026nbsp;\u0026times;\u0026nbsp;0.50\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eThe UK Met Office Hadley Centre for Climate Change (UK)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eHadGEM3-GC31-MM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.23\u0026deg;\u0026nbsp;\u0026times;\u0026nbsp;0.35\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGeophysical Fluid Dynamics Laboratory/ NOAA (U.S.)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eHiRAM-SIT-HR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.25\u0026deg;\u0026nbsp;\u0026times;\u0026nbsp;0.25\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eHiRAM-SIT-LR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.50\u0026deg;\u0026nbsp;\u0026times;\u0026nbsp;0.50\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMax Planck Institute MPI, (Germany)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eMPI-ESM1-2-HR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.94\u0026deg;\u0026nbsp;\u0026times;\u0026nbsp;0.94\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eMPI-ESM1-2-XR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.47\u0026deg;\u0026nbsp;\u0026times;\u0026nbsp;0.47\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMeteorological Research Institute (Japan)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eMRI-AGCM3-2-H\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.56\u0026deg;\u0026nbsp;\u0026times;\u0026nbsp;0.56\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eMRI-AGCM3-2-S\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.19\u0026deg;\u0026nbsp;\u0026times;\u0026nbsp;0.19\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35.3334px;\"\u003e\n\u003ctd style=\"height: 35.3334px;\" align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70.3334px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;Japan Agency for Marine-Earth Science and Technol\u0026nbsp;Ogy (Japan)\u003c/div\u003e\n\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.3334px;\" align=\"left\"\u003e\n\u003cp\u003eNICAM16-7S\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.3334px;\" align=\"left\"\u003e\n\u003cp\u003e0.56\u0026deg;\u0026nbsp;\u0026times;\u0026nbsp;0.56\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70.3334px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eNICAM16-8S\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e0.28\u0026deg;\u0026nbsp;\u0026times;\u0026nbsp;0.28\u0026deg;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.2 Extreme precipitation indices and model validation metrics\u003c/h2\u003e\n\u003cp\u003eThis study uses extreme climate indices described by the Expert Team on Climate Change Detection and Indices (ETCCDI; [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]) to investigate future changes in precipitation extremes in the River Basin. The extreme precipitation indices are listed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and have been widely applied to study changes in future precipitation extremes around the globe [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e] In particular, this study uses the indices that mainly represent the intensity and frequency of extreme precipitation. These indices are employed herein to study the seasonally dependent near-future changes in precipitation extremes over the Anambra-Imo River Basin.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDescription and unit of extreme precipitation indices analyzed\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eName\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDescription\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUnits\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePRCPTOT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal wet-day precipitation amount\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal precipitation in wet days (P\u0026thinsp;\u0026ge;\u0026thinsp;1 mm), defined as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{ij}\\)\u003c/span\u003e\u003c/span\u003e representing daily precipitation amount on day \u003cem\u003ei\u003c/em\u003e in a period \u003cem\u003ej\u003c/em\u003e. If the \u003cem\u003ei\u003c/em\u003e denote the number of days in \u003cem\u003ej\u003c/em\u003e, then; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({PRCPTOT}_{j}=\\sum _{i=1}^{i}{P}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emm\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRX5day\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum consecutive 5-day precipitation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum rainfall sum for 5-day interval. Let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{ij}\\)\u003c/span\u003e\u003c/span\u003e be the precipitation amount for the 5-day interval \u003cem\u003ei\u003c/em\u003e ending in period \u003cem\u003ej\u003c/em\u003e. The maximum 5-day values are estimated for the period as: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({RX5day}_{j}\\)\u003c/span\u003e\u003c/span\u003e = max(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{ij}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emm\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eR10mm\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeavy precipitation days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of very heavy precipitation days (P\u0026thinsp;\u0026ge;\u0026thinsp;10mm). That is; let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{ij}\\)\u003c/span\u003e\u003c/span\u003e be the daily precipitation amount where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{ij}\\ge 10 mm\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edays\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSDII\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWet-day intensity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAverage precipitation from wet-days. This can be defined as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{wj}\\)\u003c/span\u003e\u003c/span\u003e be the daily precipitation amount on wet days, \u003cem\u003ew\u003c/em\u003e (P\u0026thinsp;\u0026ge;\u0026thinsp;1 mm) in period \u003cem\u003ej\u003c/em\u003e. If \u003cem\u003ew\u003c/em\u003e represents number of wet days in \u003cem\u003ej\u003c/em\u003e, then \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({SDII}_{j}=\\frac{\\sum _{w=1}^{w}{P}_{wj}}{W}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emm/day\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCWD\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ewet spells\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum number of consecutive dry days (P\u0026thinsp;\u0026ge;\u0026thinsp;1 mm). Let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{ij}\\)\u003c/span\u003e\u003c/span\u003e be the daily precipitation amount on day \u003cem\u003ei\u003c/em\u003e in period \u003cem\u003ej\u003c/em\u003e. Count the largest consecutive days where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{ij}\\ge 1mm\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edays\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCDD\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edry spells\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum number of consecutive dry days (P\u0026thinsp;\u0026le;\u0026thinsp;1 mm). Let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{ij}\\)\u003c/span\u003e\u003c/span\u003e be the daily precipitation amount on day \u003cem\u003ei\u003c/em\u003e in period \u003cem\u003ej\u003c/em\u003e. Count the largest consecutive days where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{ij}\\le 1mm\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edays\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFurthermore, we used statistical evaluation metrics such as mean bias (MB), the normalized root mean square error (NRMSE) and the Pattern correlation coefficient (PCC). The mean bias for each model is calculated with respect to the observations as follows; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(bias=\\frac{\\left(\\overline{x}-\\overline{y}\\right)}{\\text{y}}\\)\u003c/span\u003e\u003c/span\u003e where \u003cstrong\u003ex\u003c/strong\u003e and \u003cstrong\u003ey\u003c/strong\u003e denote the map of each CMIP6 HighResMIP model and the observed field, respectively, and the over-bars (\u003csup\u003e\u0026ndash;\u003c/sup\u003e) indicate their means. The MB provides information on the difference in magnitude between observation and models.\u003c/p\u003e\n\u003cp\u003eThe PCC between the model and observation is defined as follows.\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$PCC= \\frac{1}{{\\phi }_{x}{\\phi }_{y}}\\left[\\frac{1}{N}\\sum _{i=1}^{N}\\left({x}_{i}-\\stackrel{-}{x}\\right)({y}_{i}-\\stackrel{-}{y})\\right]$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eSillmann et al., [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e] used the normalized root-mean-square error (NRMSE), among other metrics to evaluate the performance of climate models. The NRMSE is also computed for the extreme precipitation indices following the same approach.\u003c/p\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equb\" class=\"mathdisplay\"\u003e$$NRMSE= \\frac{\\sqrt{\\frac{1}{N}\\sum _{i=1}^{N}{({x}_{i}-{y}_{i})}^{2}}}{\\frac{1}{N}\\sum _{i=1}^{N}{y}_{i}}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere N is the number of grid cells, \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e are the variable values to the ith grid cell of the models and the observed field, respectively. Whereas the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\overline{x}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\overline{y}\\)\u003c/span\u003e\u003c/span\u003e their mean values, \u003cem\u003e\u0026phi;\u003c/em\u003e\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003e\u0026phi;\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e their standard deviations. Both NRMSE and PCC provide information on the model performance.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.3 Analysis of the projected future changes\u003c/h2\u003e\n\u003cp\u003eThe projected near-future changes in precipitation extremes are quantified by the relative change between the projection (2031\u0026ndash;2050) period and the baseline period (1995\u0026ndash;2014). The future projections are considered for four seasons to obtain seasonal-dependent changes in precipitation extremes for the river basin. Moreover, we consider the projected changes robust when the ratio of the mean to the standard deviation of the climate change signals is equal to or larger than one. As such, when at least 70% of the models agree to defined metric, we consider the sign of the projected change in the precipitation extremes robust. We determined statistically significant projected change based on student's t test at 95% confidence level.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"3.0 Results and Discussions","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e3.1 Performance of the CMIP6 HighResMIP models in representing observed precipitation extremes\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the spatial distribution of observed annual mean precipitation extremes, multi-model ensemble mean (MME) of HighResMIP models as well as their mean bias relative to the observation. The observation shows a high and low magnitude of annual total precipitation (PRCPTOT) over the lower reaches and upper part of the Anambra-Imo River basin, respectively, during the 1995\u0026ndash;2014 period. Similarly, annual heavy precipitation days (R10mm), maximum 5-day precipitation (RX5day), wet-day intensity (SDII), and wet spells (CWD) follow the same pattern in the river basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-e). Conversely, it is found that the dry spell is more prevalent over the part of the river basin encompassing the northern extremities of Anambra, Enugu, and Ebonyi states (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef\u003cb\u003e)\u003c/b\u003e. Interestingly, the MME reproduced the observed annual precipitation extremes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg-l\u003cb\u003e)\u003c/b\u003e, albeit indications that the models overestimated the magnitude of the observed annual precipitation extremes. The models overestimated RX5day, PRCPTOT, R10mm, CWD, SDII, and CDD \u003cb\u003e(Fig. m-r)\u003c/b\u003e. Specifically, it is found that the models overestimated PRCPTOT, R10mm, and SDII by 60%, 10%, and 40%, respectively, whereas the magnitude of the mean bias is about 80% for CWD, RX5day, and CDD during the 1995\u0026ndash;2014 period. The foregoing indicates that biases known to exist in GCMs also persist in the HighResMIP simulation of precipitation extremes. The biases in the HighResMIP simulated precipitation extremes are perhaps related to the resolution of the models. Although the HighResMIP is acclaimed for its high resolution, nearly half of the models used in this study have a grid spacing larger than 0.5\u0026deg;, affecting the model's performance in the river basin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, we examine the performance of the models by considering the pattern correlation coefficients (PCC) and normalized root mean square errors (NRMSE) for the 35 grid cells within the river basin \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-b\u003cb\u003e)\u003c/b\u003e. Results show that the models creditably reproduced the spatial pattern of the annual precipitation extremes with PCC\u0026thinsp;\u0026gt;\u0026thinsp;0.8 for MME. Particularly, FGOALS-f3-L, CMCC-CM2-HR4, HadGEM3-GC31-MM, MPI-ESM1-2-XR, NICAM16-7S, and NICAM16-8S performed better than other models with PCC\u0026thinsp;\u0026gt;\u0026thinsp;0.8 for all the extreme precipitation indices. Meanwhile, some of the models show lesser performance in reproducing the spatial pattern of SDII over the river basin, with PCC\u0026thinsp;\u0026le;\u0026thinsp;0.4. It is also found that the models simulated the climatologies of annual mean precipitation extremes with minimum errors by CMCC-CM2-HR4, GFDL-CM4C192, HadGEM3-GC31-MM, HiRAM-SIT-HR, MPI-ESM1-2-XR, MPI-ESM1-2-HR, and NICAM16-7S (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Meanwhile, the NRMSEs are consistently large for CWD and CDD, this is perhaps linked to the inability of the models in simulating the number of rainy days [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and resolving the complex terrain over the Guinea coast [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Generally, the models captured the observed features of annual mean precipitation extremes over the Anambra-Imo river basin during the 1995\u0026ndash;2014 period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Annual and seasonal dependent changes in precipitation extremes\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e depicts the spatial pattern of the projected changes in annual mean precipitation extremes during 2031\u0026ndash;2050 compared to 1995\u0026ndash;2014. The multi-model projection of near-term precipitation extremes indicates an increase in annual total precipitation (PRCPTOT), maximum 5-day precipitation (RX5day), and heavy precipitation days (R10mm); the results further indicate that the high-resolution models project shows a robust increase in extreme precipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-c). Meanwhile, the HighResMIP models project no increase in annual wet-day intensity (SDII), whereas a non-significant increase is projected for wet spells over Enugu, Anambra, Ebonyi, and Abia states (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed-e). Besides, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef shows a non-significant decrease in dry spells over the river basin. The foregoing indicates that in the near-future, the annual increase in PRCPTOT is mostly associated with the projected increase in precipitation intensity rather than its frequency. The projected increase in RX5day is consistent with the results obtained by Salihu et al., [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] for the Guinea Coast region of Nigeria based on CMIP5 models. Besides, Salihu et al., [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] reported a significant increase in RX5day during the present-day period; this suggests that future increases in the precipitation extremes will trigger more disaster flood disasters in the region. Diba et al., [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] reported that the intensification of precipitation over West Africa is related to the warm extremes. According to the Clausius\u0026ndash;Clapeyron equation, global warming has accelerated the local water cycle and increased advected moisture, leading to the intensification in precipitation extremes [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we discuss the projected changes in precipitation extremes at seasonal timescales. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the projected changes in March-April-May (MAM) precipitation extremes over the Anambra-Imo River basin. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea shows that the western parts of the river basin, specifically Anambra and Imo states, will experience a non-significant decrease in PRCPTOT during the spring season while other parts of the river basin will record a non-significant increase in PRCPTOT. A similar pattern will be recorded for RX5day, R10mm, SDII, and CWD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-e). However, it should be noted that most parts of Enugu, Ebonyi, and Abia states will experience an increase in precipitation intensity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec-d), while a dry spell will increase during the MAM season (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInterestingly, the models project a robust and statistically significant increase in summer (JJA) precipitation extremes over the river basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-d). The increase in summer PRCPTOT is mostly linked to intensity-related extremes viz RX5day and SDII, especially over Enugu, Ebonyi, and Abia states. Although the projected increase in R10mm and CWD is not statistically significant, the decrease in CDD suggests that the river basin will experience an increase in intensity and less frequent summer precipitation extremes. This is consistent with the findings by Dike, Lin and Ibe [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] for the sub-region in recent decades.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the spatial distribution of the projected changes in September-October-November shows that in the near-future, the river basin will experience a greater magnitude of increase in precipitation extremes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The models project a wide-spread, robust, and significant increase in PRCPTOT, RX5day, R10mm, and SDII (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea-d). Essentially, the state in the northern fringes of the river basin (Anambra, Enugu, and Ebonyi) will record a notable increase in the precipitation extremes. The precipitation is projected to increase by up to 46mm, 1.2 days, 24mm, and 2.4mm/day for PRCPTOT, R10mm, RX5day, and SDII, respectively. It is noteworthy to state that the projected changes are robust as more than 70% of the models herein considered agree with the projected significant increase in SON precipitation extremes over the river basin. This increases the vulnerability of the river basin to flood disasters in the future [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Meanwhile, the projected change in CWD shows a non-homogenous increase (decrease) in wet spells, whereas the projected decrease in CDD is widespread (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee-f). As with the findings for the summer season, the projected increase in the extremes is also linked to the increase in the intensity and less frequent precipitation extremes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Both the JJA and SON seasons will experience a significant and severe increase in precipitation extremes, with higher magnitude recorded in the SON season. Specifically, Hassan et al., [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] used the downscaled CMIP5 model to demonstrate that mean precipitation will increase significantly in July-August-September in most parts of the Niger-Delta region. This indicates that water vapour will be more abundant during the July-August-September season in the near future. Meanwhile, previous studies have illustrated that future precipitation in the region will be characterized by intense precipitation extremes and highlighted the propensity of later wet seasons with more intense precipitation under future climate change [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFinally, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows that during the December-January-February (DJF) season, PRCPTOT, RX5day, R10mm, and CDD will increase slightly. Specifically, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea-c shows that winter PRCPTOT, RX5day, and R10mm will increase in most parts of the river basin. No notable change is projected for SDII for the DJF season over the river basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed). However, RX5day will increase significantly by 10mm over most parts of Imo state (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb), while CWD will increase (decrease) by 1 day relative to the present-day levels in the southern (northern) parts of the river basin. Remarkably, CDD will increase slightly in the river basin as well, suggesting that both wet and dry precipitation extremes will increase in the future during the DJF season (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea-f). More so, the increase in the precipitation extremes is projected to extend to the DJF season, which indicates a wet winter season in the river basin under SSP5-8.5 emission scenario. Studies have indicated the changes in precipitation seasonality over the region, especially under high-emission scenarios [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMeanwhile, the projected increase in the seasonal precipitation extremes, especially during the JJA, SON, and DJF seasons, contributes significantly to the projected annual precipitation extremes in the near future. The significant increase in the intensity-related precipitation extremes implies a potential risk of intensified extreme precipitation and the associated flood disasters [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], which would increase the vulnerability of key socioeconomic sectors, such as agriculture and water management under SSP5-8.5 emission scenario [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 Conclusion","content":"\u003cp\u003eThis study investigates the projected future changes in seasonal precipitation extremes over Anambra-Imo River basin in southeastern Nigeria. Firstly, fifteen AMIP-type historical simulations of the CMIP6 HighResMIP were evaluated against an ensemble of two gridded observation products to assess the ability of the models to reproduce the observed precipitation extremes. The results show that the models creditably reproduced the spatial distribution of the observed precipitation extremes. Specifically, CMCC-CM2-HR4, FGOALS-f3-L, HadGEM3-GC31-MM, MPI-ESM1-2-XR, NICAM16-7S, and NICAM16-8S performed better than other models with PCC\u0026thinsp;\u0026gt;\u0026thinsp;0.8 for all the extreme precipitation indices. Consistent with the results obtained by Ajibola et al., [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] for the entire West Africa region. Although the models seemingly overestimated the precipitation extremes, it is also found that the models simulated the climatologies of annual mean precipitation extremes with minimum errors. Specifically, CMCC-CM2-HR4, GFDL-CM4C192, HadGEM3-GC31-MM, HiRAM-SIT-HR, MPI-ESM1-2-XR, MPI-ESM1-2-HR, and NICAM16-7S simulated the precipitation extreme with minimal errors. However, the NRMSEs are consistently large for CWD and CDD; this is perhaps linked to the inability of the models to simulate the number of rainy days over the river basin.\u003c/p\u003e \u003cp\u003eFurthermore, the HighResMIP models project a significant increase in annual total wet-day precipitation (PRCPTOT) for 2031\u0026ndash;2050 relative to the 1995\u0026ndash;2014 period under the SSP5-8.5 emission scenario. The projected increase in PRCPTOT is mostly linked to the increase in maximum 5-day precipitation (RX5day) and heavy precipitation (R10mm). Consistent with the projected increase in these precipitation extremes indices, the future total annual dry spells will decrease in the river basin. It was also found that during the MAM season, most parts of Enugu, Ebonyi, and Abia will experience an increase in R10mm and SDII, leading to an increase in MAM PRCPTOT in these aforementioned areas. However, it should be noted that the projected increase in these extreme precipitation indices is not statistically significant and has notable uncertainty. Meanwhile, the models project an increase in dry spells during the MAM season, which indicates a drier spring over the river basin in the near-future.\u003c/p\u003e \u003cp\u003eNonetheless, the HighResMIP models project a notable increase in summer precipitation extremes. Results further demonstrate that summer PRCPTOT, R10mm, RX5day, and SDII will increase in most parts of the river basin, especially over the northern reaches of the Anambra-Imo River Basin encompassing Enugu, Ebonyi and Abia state. The spatial distribution of consecutive wet days shows that wet spells will also increase over these areas, whereas dry spells will decrease. Relatedly, precipitation extremes during the September-October-November (SON) will be characterized by a similar pattern projected for the summer season but with a higher magnitude. This is an indication that the river basin will experience an increase in intensity and less frequent summer precipitation extremes in the near future, especially during the SON season. Similar results have also been obtained for other river basins worldwide [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. We note that in the near future, the SON will characterized by a higher magnitude increase in precipitation extremes, which will potentially exacerbate flash flooding in the river basin during the season. We highlight the need for a detailed analysis of the potential physical mechanisms driving the projected changes in the precipitation extremes in the river basin.\u003c/p\u003e \u003cp\u003eMeanwhile, for the December-January-February (DJF) season, the models project a marginal increase in winter precipitation extremes like RX5day and R10mm, with a higher magnitude of RX5day over the Imo state. This suggests that the fundamentally dry season will be wetter in the near future. Additionally, the river basin will experience more intense precipitation during the JJA and SON seasons, contributing significantly to the projected increase in annual total precipitation in the river basin. It is also important to note that in the near future, precipitation extremes in the river basin will be characterized by more intense and less frequent precipitation extremes, potentially exacerbating flash flooding in the vulnerable river basin [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Meanwhile, we presented the future changes in precipitation extremes in the river basin without analyzing the mechanism associated with these projected changes. Therefore, further analysis is needed to identify the circulation features associated with these projected changes. Besides, one notable limitation of this study is the horizontal resolution of some of the HighResMIP models used in this study. The spatial resolutions of the models are still coarse, considering the size of the river basin. Therefore, further studies over the river basin should consider higher resolution regionally downscaled models. Despite these limitations, the inferences drawn from this study will provide a vital resource needed for near-term socio-economic planning and adaptation measures that will minimize the impact of flood disasters in the Anambra-Imo River Basin.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e\n\u003cp\u003eNo conflict of interest. All the authors agree with the content of this study.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eICC: Conceptualization; data curation; formal analysis; writing \u0026ndash; original draft; writing \u0026ndash; review and editing. DVN: Conceptualization; data curation; formal analysis; funding acquisition; methodology; writing \u0026ndash; original draft; writing \u0026ndash; review and editing. SKI and MJI: Supervision; writing \u0026ndash; original draft; writing \u0026ndash; review and editing. IAA and ACM: Writing \u0026ndash; review and editing.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors thankfully acknowledge the funding from the NSFC research fund for international young scientists (Grant No. 42150410394) and the support from the National Key Scientific and Technological Infrastructure project \u0026ldquo;Earth System Science Numerical Simulator Facility\u0026rdquo; (EarthLab).\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are openly available at the following sources:\u003c/p\u003e\n\u003cp\u003eHighResMIP: \u0026nbsp;https://esgf-node.llnl.gov/projects/cmip6/\u003c/p\u003e\n\u003cp\u003eCHRIPS: http://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/p25/\u003c/p\u003e\n\u003cp\u003eMSWEP: https://www.gloh2o.org/mswep/\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOgungbenro, S.B.; Morakinyo, T.E. 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Earth's Future 2020, \u003cem\u003e8\u003c/em\u003e, e2019EF001331, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2019EF001331\u003c/span\u003e\u003cspan address=\"10.1029/2019EF001331\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-atmosphere","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Atmosphere](https://www.springer.com/journal/44292)","snPcode":"44292","submissionUrl":"https://submission.nature.com/new-submission/44292","title":"Discover Atmosphere","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Precipitation extremes, CMIP6, HighResMIP, Anambra-Imo River Basin, Nigeria","lastPublishedDoi":"10.21203/rs.3.rs-4303083/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4303083/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe southeastern region of Nigeria is susceptible to flood disasters primarily triggered by extreme precipitation with localized impacts. This study uses the Coupled Model Intercomparison Project Phase 6 (CMIP6), High-Resolution Model Intercomparison Project (HighResMIP) to investigate seasonal dependent changes in precipitation extremes in the near future (2031\u0026ndash;2050) in the Anambra-Imo River Basin, in the southeastern region of Nigeria. Evaluating the models against observation for the 1995\u0026ndash;2014 period, it is found that models creditably reproduced the spatial pattern of the observed annual precipitation extremes over the river basin. Results show that in the near future, annual precipitation extremes will be characterized by a robust increase in annual total precipitation amount (PRCPTOT), maximum 5-day precipitation (RX5day), and heavy precipitation (R10mm). Meanwhile, the models project a significant increase in PRCPTOT, RX5day, R10mm, and wet-day intensity (SDII) for the June-July-August (JJA) and September-October-November (SON) seasons. The results demonstrate a robust and higher magnitude increase in precipitation extremes during the SON season. Specifically, PRCPTOT, RX5day, R10mm and SDII are projected to increase by up to 46 mm, 24 mm, 1.2 days and 2.4 mm/day, respectively. Whereas during the March-April-May (MAM) season, the HighResMIP suggests that PRCPTOT, R10mm, and SDII will marginally increase over the eastern part of the Anambra-Imo River Basin. Besides, the December-January-February (DJF) season will be characterized by a marginal increase in the precipitation extremes, especially over the southern fringes of the river basin. We note that in the near future, precipitation extremes in the river basin will be characterized by more intense and less frequent precipitation extremes during the JJA and SON, potentially exacerbating flash flooding in the river basin. Hence, the results of this study may be vital for near-term socio-economic planning and policy decisions that will minimize the impact of flood disasters in the Anambra-Imo River Basin.\u003c/p\u003e","manuscriptTitle":"Projected near-future changes in precipitation extremes over Anambra-Imo River Basin inferred from CMIP6 HighResMIP","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-09 05:05:53","doi":"10.21203/rs.3.rs-4303083/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"163767414253644551889254726083503411352","date":"2024-05-04T04:34:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-03T13:21:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-03T13:15:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-03T13:13:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Atmosphere","date":"2024-04-22T04:41:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-atmosphere","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Atmosphere](https://www.springer.com/journal/44292)","snPcode":"44292","submissionUrl":"https://submission.nature.com/new-submission/44292","title":"Discover Atmosphere","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d1b3c3f0-ea78-4ecb-b9fb-d78fbd0fcc7d","owner":[],"postedDate":"May 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-12T11:47:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-09 05:05:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4303083","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4303083","identity":"rs-4303083","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}