The rising sea levels due to climate change are a significant concern, particularly for vulnerable, low-lying coastal regions like the Gulf of Guinea (GoG). To effectively address this issue, it is crucial to gain a comprehensive understanding of historical sea level variability, and the influencing factors, and develop a reliable modeling system for future projections. This knowledge is essential for informed planning and mitigation strategies aimed at protecting coastal communities and ecosystems. This study presents a comprehensive analysis of mean sea level anomaly (MSLA) trends in the GoG between 1993 and 2020, covering three distinct periods (1993–2002, 2003–2012, and 2013–2020). It investigates the connections between interannual sea level variability and large-scale oceanic and atmospheric forcings. Furthermore, the study evaluates the performance of supervised machine learning techniques to optimize sea level modeling. The findings reveal a consistent rise in MSLA linear trends across the basin, particularly pronounced in the northern region, with a total linear trend of 88 mm over the entire period. The highest decadal trend (38.7 mm) emerged during 2013–2020, with the most substantial percentage increment (100%) occurring in 2003–2012. Spatial variation in decadal sea-level trends was influenced by subbasin physical forcings. Strong interannual signals in the spatial sea level distribution were identified, linked to large-scale oceanic and atmospheric phenomena. Seasonal variations in sea level trends are attributed to seasonal changes in the forcing factors. The evaluation of supervised learning modeling methods indicates that Random Forest Regression and Gradient Boosting Machines are the most accurate, reproducing interannual sea level patterns in the GoG with 97% and 96% accuracy. These models could be used to derive regional sea level projections via downscaling of climate models. These findings provide essential insights for effective coastal management and climate adaptation strategies in the GoG.

The Gulf of Guinea is undergoing accelerated sea-level rise (SLR), with localized rates surpassing 10 mm yr⁻¹, more than double the global mean. Integrating GRACE/FO ocean mass data, reanalysis products, and machine learning, we identify a regime shift in the regional sea-level budget post-2015. Over 60% of observed SLR near major riverine outlets stems from ocean mass increase, driven primarily by intensified terrestrial hydrological discharge, marking a transition from steric to barystatic and manometric dominance. This shift coincides with enhanced monsoonal precipitation, wind-forced equatorial wave adjustments, and Atlantic–Pacific climate coupling. Piecewise regression reveals a significant 2015 breakpoint, with mean coastal SLR rates increasing from 2.93 ± 0.1 to 5.4 ± 0.25 mm yr⁻¹ between 1993 and 2014, and 2015 and 2023. GRACE data indicate extreme mass accumulation (>10 mm yr⁻¹) along the eastern Gulf coast, tied to elevated river discharge and estuarine retention. Dynamical analysis reveals the reorganization of wind field intensification, which modifies Rossby wave dispersion and amplifies zonal water mass convergence. Random forest modeling attributes 16% of extreme SLR variance to terrestrial runoff (comparable to wind stress at 19%), underscoring underestimated land–ocean interactions. Current climate models underrepresent manometric contributions by 20–45%, introducing critical projection biases for high-runoff regions. The societal implications are severe, with >400 km² of urban land in Lagos and Abidjan vulnerable to inundation by 2050. These findings reveal a hybrid steric–manometric regime in the Gulf of Guinea, challenging existing paradigms and suggesting analogous dynamics may operate across tropical margins. This calls for urgent model recalibration and tailored regional adaptation strategies.

The rising sea level due to climate change poses a critical threat, particularly affecting vulnerable low-lying coastal areas such as the Gulf of Guinea (GoG). This impact necessitates precise sea level prediction models to guide planning and mitigation efforts for safeguarding coastal communities and ecosystems. This study presents a comprehensive analysis of mean sea level anomaly (MSLA) trends in the GoG between 1993 and 2020. The assessment covers three distinct periods (1993–2002, 2003–2012, and 2013–2020) and investigates connections between interannual sea level variability and large-scale oceanic and atmospheric forcings. Additionally, the performance of artificial neural networks (LSTM and MLPR) and machine learning techniques (MLR, GBM, and RFR) is evaluated to optimize sea level predictions. The findings reveal a consistent rise in MSLA linear trends across the basin, particularly pronounced in the north, with a total linear trend of 88 mm/year over the entire period. The highest decadal trend (38.7 mm/year) emerged during 2013–2020, and the most substantial percentage increment (100%) occurred in 2003–2012. Spatial variation in decadal sea-level trends was influenced by subbasin physical forcings. Strong interannual signals in the spatial sea level distribution were identified, linked to large-scale oceanic and atmospheric phenomena. Seasonal variations in sea level trends are attributed to seasonal changes in the forcing factors. Model evaluation indicates RFR and GBR as accurate methods, reproducing interannual sea level patterns with 97% and 96% accuracy, respectively. These findings contribute essential insights for effective coastal management and climate adaptation strategies in the GoG.

The Gulf of Guinea is experiencing accelerated sea-level rise (SLR), with localized rates exceeding 10 mm yr⁻¹, more than twice the global average. Integrated analysis of GRACE/FO ocean mass data, reanalysis products, and machine learning reveals a fundamental regime shift in the regional sea-level budget after 2015. Ocean mass increase, primarily driven by intensified terrestrial hydrological discharge, accounts for over 60% of observed SLR near major riverine outlets, indicating a transition from steric to barystatic-manometric dominance. This transition aligns with increased monsoonal precipitation, wind-driven changes in equatorial wave dynamics, and coupled Atlantic-Pacific climate variability. Piecewise regression identifies a significant 2015 breakpoint, with mean coastal SLR rates rising from 2.93 ± 0.1 to 5.4 ± 0.25 mm yr⁻¹. GRACE data show extreme mass accumulation (>10 mm yr⁻¹) along the eastern Gulf coast, strongly linked to elevated river discharge and estuarine retention. Physical analysis indicates that intensified wind stress curl has altered Rossby wave dispersion, enhancing zonal water mass convergence. Random Forest modeling attributes 16% of extreme SLR variance to terrestrial runoff, close to wind stress’s 19%, highlighting underestimated land-ocean coupling. Current global climate models underrepresent these manometric contributions by 20–45%, causing critical biases in projections for high-runoff regions. The societal impact is severe, with over 400 km² of urban land in Lagos and Abidjan at risk of inundation by 2050. These findings expose a hybrid steric-manometric regime in the Gulf of Guinea, challenging existing paradigms and indicating similar dynamics may affect tropical margins globally. This calls for urgent model recalibration and tailored regional adaptation strategies.