Why has climate change affected rainfall patterns and climatic factors in the wettest places on Earth, Debunka, or the Gulf of Guinea in Africa?

Debundscha is ranked amongst the top rainiest places on Earth, yet there is a paucity of information on rainfall trends and their drivers in the area. However, the increasing frequency and intensity of hydrometeorological disasters, declining crop yields, and rising water scarcity in the area underscore the urgent need to investigate changing rainfall patterns and the climatic modes influencing rainfall in the area. Trend analysis using the Mann-Kendall test shows non-statistically significant declining trends in annual and seasonal rainfall in Debundscha (1972–2009) and other stations, except Limbe, which exhibits statistically significant increasing trends in annual and seasonal rainfall (1985–2015). Statistical analysis using partial least squares regression reveals that the Atlantic Multidecadal Oscillation (AMO), Southern Oscillation Index, Indian Ocean Dipole Western Pool Index, global sea surface temperature (GSST), Multivariate ENSO Index, and El Niño-Southern Oscillation are the dominant climatic modes influencing annual and seasonal rainfall in the study area. However, a different set of climatic modes affects rainfall at each station, with AMO and GSST strongly associated with annual rainfall in Debundscha. Analysing rainfall in high precipitation areas like Debundscha is essential for understanding how these regions are affected by changing climate patterns. On the other hand, identifying the main climatic modes influencing local rainfall is crucial for creating accurate seasonal and sub-seasonal forecasts. These forecasts can help reduce hydrometeorological risks, improve water management, and increase agricultural productivity .Across the world, rainfall patterns are changing as a result of climate change and variability – thereby increasing the frequency and severity of extreme hydrometeorological events such as droughts and floods (Haile et al. 2019; Konapala et al. 2020). Droughts caused by changing rainfall patterns can lead to water scarcity and resource conflicts especially in areas where the population depends on rainfed agriculture for livelihoods (Nkiaka, Bryant, and Kom 2024). It could also lead to poor vegetation health and food insecurity (Barbosa and Asner 2017; Wei et al. 2023). Floods also have substantial socioeconomic impact on the society and may result to billions of dollars in direct economic loss, cause disruption to global trade and in extreme cases lead to fatalities (Merz et al. 2021). Due to the numerous undesirable consequences of such disasters, substantial efforts have been made to understand the effects of changing rainfall patterns on drought and flood frequency and severity from global to regional scales (Roudier et al. 2016; Haile et al. 2019; Barendrecht et al. 2024). The high number of such studies may be attributed to the fact that their impacts usually transcend geographical boundaries and also because the mechanisms controlling their onset and propagation are mostly global in nature (Dimri et al. 2016; Nied et al. 2017; Gimeno- Sotelo et al. 2024). To support community adaptation to changing rainfall patterns under increasing climate change, and attend the United Nations Sustainable Development Goals, it is crucial to understand the effects of such changes at the local scale where the impacts are more visible, and adaptation measures can be more effective and beneficial to the local communities.

Several studies have analysed changing rainfall patterns and the dominant climatic modes influencing rainfall variability in some of the rainiest places on Earth. For example, rainfall analysis in northern India revealed that the rainiest place on Earth has shifted from Cherrapunji to Mawsynram (Kuttippurath et al. 2021). The same study also reported that changes in Indian Ocean and Arabian Sea temperatures are strongly associated with rainfall variability in the two localities (Kuttippurath et al. 2021). Mejía et al. (2021) reported that the ChocoJet, Caribbean Low- Level Jet, Panama semi- permanent low and other mesoscale convective systems are responsible for rainfall variability in the Far Eastern Tropical Pacific and Western Colombia which are also ranked amongst the rainiest places on Earth. Frazier and Giambelluca (2017) analysed spatial trends in rainfall in the state of Hawai including the Maui Mountain which has some of the rainiest places on Earth. Although Debundscha is ranked amongst the top rainiest places on Earth (Richards 1952; WMO 2024), to our knowledge, there is no study that has investigated changes in rainfall patterns and the climatic modes influencing rainfall variability in area. As a result, there is limited knowledge about rainfall patterns in Debundscha, and the key climatic factors influencing rainfall in this location and nearby areas remain poorly understood.

Despite the limited knowledge on rainfall patterns and the climatic modes influencing rainfall in Debundscha and neighbouring localities, recent high- impact hydrometeorological events in the area including floods (Findi et al. 2022; Enomah et al. 2023; Kum et al. 2023; Dohnji et al. 2024), decline in crop yield (Sounders et al. 2017; Wanie et al. 2020) and increasing water scarcity (Nkiaka 2022; Fonjong and Zama 2023) highlight the urgent need to understand the changes in rainfall patterns and the climatic drivers controlling rainfall in the region. Understanding changing rainfall patterns in Debundscha and neighbouring localities may be critical for agricultural investment. Likewise, understanding the dominant climatic modes associated with rainfall may be useful for providing seasonal climate forecasts to boost agricultural productivity, enhance water management, mitigate disaster risk and adapt to the impacts of climate change.

From the above, the objectives of this study are to (1) analyse the spatio- temporal distribution of rainfall and its trends in Debundscha and neighbouring stations and (2) identify the climatic modes influencing annual and seasonal rainfall in the region. This study contributes to the contemporary literature on the changing rainfall patterns and their drivers in areas with high annual rainfall.

2 | Materials and Method

Debundscha is a village located at the foothills of Mount Fako (aka Mount Cameroon) on the southwestern flanks of the mountain directly facing the Atlantic Ocean. Debundscha is ranked amongst the top the rainiest places on Earth with annual rainfall exceeding 10,000 mm/year (Richards 1952; WMO 2024). The geographic coordinates of the locality are 4.11° N and 8.99° E. High annual rainfall in Debundscha has been attributed to orographic precipitation as moisture- laden oceanic air is forced to rise in a short distance up and over the southwestern slopes of Mount Fako, consequently unloading the moisture over a small area of land and enhancing local monsoon effect (Fraser et al. 1998). The Mount Fako region has a tropical climate with one wet and one dry season and is classified as a “tropical rainy” (A) category of the Koppen climate classification with more of a ‘tropical monsoonal rainfall regime’ (Am) along the Atlantic Coast (Fraser et al. 1998). Rainfall in the whole region is controlled by the west African monsoon system characterised by the seasonal displacement of the intertropical convergence zone (ITCZ). The mean annual temperature in Debundscha is about 25.6°C with April being the hottest month (mean = 26.6°C) while August is the coldest (mean = 24.6°C) (Fraser et al. 1998).

Due to the rich volcanic soil in the region, high annual rainfall, and high returns in agricultural conversion (Carrasco et al. 2017), large- scale agriculture is the main economic activity in the Mount Fako region, with thousands of hectares dedicated to monoculture plantations. These include oil palm plantations in Idenau, Debundscha, and Limbe; banana plantations in Likomba; tea plantations in Tole; and rubber plantations in Meanja. There are also several small- scale farmers involved in cocoa, plantain, rubber, and oil palm cultivation. However, a vast majority of the population are peasant farmers producing basic food crops such as maize, cassava, taro, beans, yams, groundnuts, and a wide variety of fresh vegetables for home consumption and petty trading. Other economic activities in the area include oil and gas exploration in the Gulf of Guinea, artisanal fishing, and timber harvesting. Abundant rainfall and rich volcanic fertile soils have made Fako and neighbouring areas the breadbasket of the central African sub- region. Many tradables such as bananas, rubber, tea, and oil palm cultivated in the region are also exported to different parts of the world. However, an ongoing armed conflict that started in 2016 between Ambazonia separatist fighters and the Government of Cameroon has significantly disrupted farming and economic activities, leading to a decline in crop yield and extreme food insecurity in the region (OCHA 2021). The armed conflict has also led to a cessation of activities and a decline in exports of tradables for many large- scale agribusinesses (Chung 2020; Bang and Balgah 2022).Monthly rainfall data used in this study was obtained from agro- industrial plantations in Idenau, Debunscha, Limbe, Likomba, Tole and Meanja (Table 1). The data was quality- controlled to ensure that it provides a representative evaluation of rainfall in the area. This was achieved by plotting time series data from the different stations to visually identify gaps in the data (Faybishenko et al. 2022). Quality control revealed that gaps in each of the rain gauge stations did not exceed 5%. Artificial neural network self- organising maps (ANN- SOM) which have been proven to be a robust method for infilling gaps in hydrometeorological time series, are used to infill the gaps (Nkiaka et al. 2016).

2.3 | Analysing Trends in Observed Rainfall in Debundscha and Neighbouring Stations

The non- parametric Mann- Kendall test and Sen's slope estimator are respectively used for trend analysis and to quantify trend magnitude. The two tests are widely used in hydrology and related studies to test the existence of monotonic increasing or decreasing trends and magnitudes in hydroclimatic timeseries. Trend analyses were conducted at the 5% significance level at the seasonal and annual timesteps for the respective periods that data are available in each station. To obtain seasonal rainfall data in this study, months in a calendar year were aggregated to a seasonal timescale as follows: dry season: December, January, and February (DJF); pre- monsoon season: March, April, and May (MAM); monsoon season: June, July, August, and September (JJAS) and post- monsoon season: October, November (ON).

2.4 | Climatic Modes Influencing Rainfall in Debundscha

Several studies have shown that different global climatic modes control annual and seasonal rainfall patterns over different regions of Africa including the central Africa and the Gulf of Guinea sub- regions (Lüdecke et al. 2021). In this study, 11 global climatic modes were used to determine the most statistically relevant climatic modes associated with seasonal and annual rainfall in the study area. These include: the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), El Niño- Southern Oscillation (ENSO3.4), Nino3, Nino4, Indian Ocean Dipole Eastern Pool Index (IODEP), Indian Ocean Dipole Western Pool Index (IODWP), global sea surface temperature (GSST), North Atlantic Oscillation (NAO), Multivariate Enso Index (MEI) Southern Oscillation Index (SOI). The climatic modes were chosen based on their easy accessibility and the datasets are freely available from http:// www. psl. noaa. gov/ data/ and https:// psl. noaa. gov/ gcos_ wgsp/ times eries/ .

Considering that there are several climatic modes that might be associated with rainfall in an area and the likelihood of interdependency amongst the different climatic modes, there is a need to identify the most influential climatic modes while eliminating the redundant ones. In this study, the variable importance in projection (VIP) from partial least squares regression (PLSR) is used to identify the climatic modes associated with seasonal and annual rainfall in the study area. The advantage of PLSR over other statistical techniques such as principal component analysis (PCA), relative importance analysis (RIA) and multivariable linear regression (MLR) is that PLSR considers the multicollinearity amongst the different climatic modes while reducing dimensionality. This is because the PLSR technique combines the strength of PCA and MLR and creates mutually orthogonal components that maximise the covariance between the independent and dependent variables. In this study, the relationship between the different climatic modes and annual and seasonal rainfall is inferred based on the regression coefficients and weights (Gonzalez- Reviriego et al. 2015; Nagaraj and Srivastav 2022). VIP is a filtering method for PLSR to select the climatic modes that strongly influence rainfall and has been used extensively to identify the climatic modes influencing rainfall in other regions (Gonzalez- Reviriego et al. 2015; Nagaraj and Srivastav 2022). It has also been used in other studies to determine the sensitivity of surface runoff to changes in climatic and environmental conditions (Nkiaka, Bryant, and Dembélé 2024). VIP scores from PLSR analysis are estimated as:

VIP = 📷SSYtotal ∙J (1)

Where Wjfis the weight value for j variable and fcomponent, and SSYf is the sum of squares of explained variance for the fth component and Jis number of independent variables in the data matrix. SSYtotal is the total sum of squares explained of the dependent variable, and F is the total number of components. W2jf is the importance of each jthvariable in fth component, VIP is a measure of the global contribution of j variable in the PLSR model. As a rule of thumb, an independent variable with a VIP score ≥ 1 suggest that the variable is statistically significant to explain the dependent variable (Gebremicael et al. 2019; Nagaraj and Srivastav 2022). A VIP score < 1 indicates a lesser contribution of the independent variable on the dependent variable. The statistical analysis to identify the different climatic modes influencing rainfall in Debundscha and neighbouring stations was conducted over the same time span that rainfall data was available at each station as shown in Table 1.

3 | Results

3.1 | Spatial and Temporal Distribution of Annual Rainfall

Figure 2 shows the temporal distribution of annual rainfall in Debundscha and neighbouring stations. It can be observed there is a strong inter- annual variability in rainfall in Idenau, Debundscha and Limbe (Figure 2). It can also be observed that 1992 was an anomalously wet year in Debundscha with annual rainfall exceeding 23,000 mm/year. Considering that no other station around the region recorded such extremely high annual rainfall in 1992, this year may be considered as an outlier (Figure 2). It could also be due to erroneous data entry caused by typographical mistakes, system glitches, or human oversight. Although the data used in this study was quality- controlled, it is worth noting that real world data may suffer from undesirable flaws such as incorrect labels, missing data, erroneous readings, and anomalies (Zha et al. 2025). Considering Debundscha alone, it can be observed that an abrupt decline in annual rainfall occurred from 1976 when maximum annual rainfall was recorded in that decade (1970). Since then, there has been a steady decline in annual rainfall except in 1992 (Figure 2). Whilst annual rainfall in Debundscha has sometimes decline to as low as 5200 mm/ year (e.g., 2007), it has always recovered above the minimum level in the years following such a decline. However, evidence show that rainfall recovery in Debundscha has not reached the level observed in 1976 (Figure 2).

It can also be observed that the inter- annual rainfall variability in the other stations (Likomba, Tole and Meanja) is not as strong as it is Debundscha. This suggest that rainfall variability is stronger in stations with high rainfall compared to stations with low annual rainfall. Considering the location of the

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FIGURE 1 | Digital Elevation Model (DEM) of the study area showing Debundscha and the other rain gauge stations. DEM data was obtained from the Shuttle Radar Topography Mission (SRTM) 30 m and processed in QGIS. The contour lines show the change in altitude from the Atlantic Ocean to the summit of Mount Fako. The basemap was obtained from Google Maps. The map in the top left- hand corner shows the location of the study area within Cameroon. [Colour figure can be viewed at wileyonlinelibrary.com]

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FIGURE 2 | Annual variability in rainfall across the different stations. [Colour figure can be viewed at wileyonlinelibrary.com]

stations (Figure 1), it may be observed that inter- annual rainfall variability is stronger in areas located close to the coast (Idenau, Debundscha and Limbe) compared to those located further inland (Likomba, Tole and Meanja). Statistical analysis also show that stations located closer the coast have higher coefficient of variation compared to those located future inland (Table 1). Analysis also shows a strong inter- decadal rainfall variability in Idenau, Debundscha and Limbe compared to the rest of the stations (Figure 2).

Figure 3 shows the monthly rainfall distribution in Debundscha and neighbouring stations. It can be observed that it rains in the region all year round; however, monthly rainfall in Debundscha and Idenau is substantially higher compared to the rest of the stations (Figure 3a,b). Whilst mean monthly rainfall for the driest months of the year (DJF) in all the stations is below 50 mm/ year, analysis shows that rainfall in Debundscha during this period exceeds 100 mm/month (Figure 3b). Taken together, Debundscha exhibits high monthly rainfall throughout the year, which may explain why it is ranked amongst the top rainiest places on Earth (Figure 3b).

Even though Debundscha exhibits high monthly rainfall throughout the year, it can be observed that monthly rainfall in Debundscha and the other stations is substantially higher during the monsoon season (JJAS) (Figure 3). Nevertheless, apart from Debundscha, which exhibits high monthly rainfall throughout the year, all the other stations in the region also exhibit high rainfall in the post- monsoon season (October and November). Furthermore, the rainfall climatology across the different stations shows that rainfall in the region is predominantly unimodal (Figure 3).

3.2 | Long- Term Trends in Annual and Seasonal Rainfall

Table 2 and Figure 4 show the long- term trends in annual rainfall across Debundscha and neighbouring stations. It can be observed that all the stations show a decline in annual rainfall except Limbe, which shows a statistically significant increase in annual rainfall at the 5% significance level (Table 2 and Figure 4). The most remarkable declines in annual rainfall are recorded in Idenau, Tole, and Debundscha with trends of −62.72, −35.30, and −22.70 mm/year respectively (Table 2). Although the trend magnitudes are high, the declines in annual rainfall in Idenau and Debundscha are not statistically significant. In contrast, the decline in Tole is statistically significant at the 1% level (Table 2).

At the seasonal timescale, all the stations except Limbe show decreasing trends in seasonal rainfall in the dry and monsoon seasons, however, these trends are not statistically significant. In contrast, Limbe exhibits statistically significant increasing trends in the monsoon season at the 1% significance level. (Table 2 and Figure 4). In the pre- monsoon season, Idenau and Debundscha show statistically significant decreasing trends at the 5% significance level while Likomba and Tole show non- statistically decreasing trends (Table 2). On the other hand, Limbe exhibits non- significant increasing trends during the pre- monsoon rainfall (Table 2 and Figure 4). Debundscha, Likomba and Tole show decreasing trends in post- monsoon rainfall, however, only Likomba demonstrate statistically significant decreasing trends at the 5% significance level (Table 2 and Figure 4). Idenau, Limbe and Meanja all exhibit non- statistically significant increasing trends in post- monsoon rainfall (Table 2). Taking together analyses show that Debundscha and Likomba exhibit decreasing trends in rainfall at both the annual and seasonal timescales. In contrast, Limbe is the only station to exhibit increasing trends in rainfall at both the annual and seasonal timescales.

Note: Dry season: December, January, February (DJF); pre- monsoon season: March, April and May, (MAM); monsoon season: June, July, August, and September (JJAS) and post- monsoon season: October and November (ON). * and ** indicate statistically significant at 5% and 1% significance levels respectively.

In this study, the climatic modes are considered as the independent (predictors) variables while rainfall is the dependent (response) variable. VIP scores based on PLSR weights are used to reveal the quantitative relationship between the different climatic modes and rainfall to identify the climatic modes with the strongest influence on annual and seasonal rainfall in the study area. As stated earlier, any climatic mode with a VIP score ≥ 1 is considered to be strong enough to influence annual or seasonal rainfall.

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FIGURE 4 | Trends in rainfall in Debundscha and its neighbouring stations. [Colour figure can be viewed at wileyonlinelibrary.com]

Figure 5 shows the different climatic modes influencing annual rainfall in Debundscha and neighbouring stations. The main climatic modes associated with annual rainfall in Idenau are the SOI, IODWP and MEI with MEI being the most influential due to its higher VIP score of 1.78 (Figure 5a). The two main climatic modes associated with annual rainfall in Debundscha are AMO and GSST with the latter being the most influential with a VIP score of 1.96 (Figure 5b). In Limbe, there are four main climatic modes influencing annual rainfall including the SOI, IODWP, GSST and MEI (Figure 5c). However, MEI is the most influential due to its higher VIP score (1.81). In Likomba, there are also four climatic modes associated with annual rainfall including AMO, SOI, GSST and MEI with the most influential being MEI which has a VIP score of 1.49 (Figure 5d). Tole has the highest number of climatic modes associated with rainfall including PDO, ENSO3.4, SOI, IODWP, MEI and Nino3 with MEI being the most influential with a VIP score of 1.53 (Figure 5e).

In Meanja, the three climatic modes associated with annual rainfall are ENSO3.4, Nino3, and Nino4, with the most influential being ENSO3.4, which has a VIP score of 2.44 (Figure 5f). Taken together, analyses show that SOI and MEI are the dominant climatic modes influencing annual rainfall in most of the stations around Debundscha (Idenau, Limbe, Likomba and Tole). This is followed by GSST and IODWP, which are both associated with annual rainfall in three stations each. GSST has a stronger influence on annual rainfall in Debundscha, Limbe, and Likomba, while IODWP has a stronger influence in Idenau, Limbe, and Tole. However, the influence of GSST is more noticeable in Debundscha due to its higher VIP score of 1.96, while that of IODWP is more noticeable in Idenau with a VIP score of 1.41. AMO, ENSO3.4, and Nino3 strongly influence annual rainfall in two stations each, while PDO and Nino4 strongly influence annual rainfall in one station each. IODEP and NAO do not show any significant influence on annual rainfall in the study area.

Figure 6 shows the different climatic modes influencing seasonal rainfall in Debundscha and neighbouring stations. In Idenau, dry season rainfall is strongly influenced by PDO and Nino3 while in Debundscha, it is strongly influenced by ENSO3.4, SOI, GSST, MEI and Nino4 (Figure 6a,b). Nevertheless, PDO and Nino4 exert a stronger influence because of their higher VIP scores of 2.02 and 1.43 respectively, in Idenau and Debundscha (Figure 6a,b). In Limbe, dry season rainfall is influenced by ENSO3.4, GSST, MEI and Nino3 with ENSO3.4 exerting a stronger influence with a VIP score of 1.64 (Figure 6c). Dry season rainfall in Likomba and Tole is influenced by SOI and MEI with both climatic modes exerting equivalent influence in each of the stations (Figure 6d,e). In Meanja, seasonal rainfall is influenced by ENSO3.4, IODWP and MEI with MEI exerting a greater influence because of its higher VIP score 1.65 (Figure 6f). Analysis suggests that MEI is the dominant climatic mode associated with dry season rainfall in Debundscha and most of the other stations (Figure 6). Pre- monsoon rainfall in Idenau is influenced by AMO, SOI, and GSST while in Debundscha, it is influenced by ENSO3.4, IODEP, Nino3 and Nino4 (Figure 6a,b). However, AMO and ENSO3.4 play a greater role in pre- monsoon rainfall because of their higher VIP scores of 1.60 and 1.81 respectively, in Idenau and Debundscha (Figure 6a,b). In the rest of the stations, pre- monsoon rainfall is influenced by several climatic modes with the dominant ones being ENSO3.4 because of its higher VIP scores of 1.38, 1.75, and 1.59 respectively, in Limbe, Likomba, and Tole (Figure 6c–f). Monsoon rainfall in Idenau and Debundscha is also influenced by several climatic modes including IODWP, GSST and MEI in Idenau and AMO, SOI, GSST and MEI in Debundscha (Figure 6a,b). Whilst IODWP is the dominant climatic mode influencing monsoon rainfall in Idenau with a VIP score of 1.88, MEI exerts a stronger influence in Debundscha with a higher VIP score of 1.51 (Figure 6a,b). In the rest of the stations, monsoon rainfall is also influenced by several climatic modes. However, GSST, AMO and ENSO3.4 with VIP scores of 1.69, 1.56, 2.01, and 2.11 respectively, are the most influential climatic modes in Limbe, Likomba, Tole and Meanja (Figure 6f). In Idenau, post- monsoon rainfall is associated with ENSO3.4, MEI, Nino3 and Nino4 while in Debundscha it is associated with SOI, IODWP, GSST and MEI (Figure 6b). However, ENSO3.4 and MEI have a stronger influence because of their higher VIP scores of 1.89 and 1.75 respectively, in Idenau and Debundscha (Figure 6b). In the rest of the stations, post- monsoon rainfall is also influenced by several climatic modes with the most influential being GSST with a VIP score of 1.67 in Likomba and ENSO3.4 which has a VIP score of 1.93, 1.45 and 2.39 respectively, in Limbe, Tole and Meanja (Figure 6d–f).

The percentage of variance explained by the various climatic modes that influence annual and seasonal rainfall in Debundscha and neighbouring stations ranges from 34% to 59.33%, 26.62% to 56.64%, 16.69% to 54.65%, 22.23% to

FIGURE 5 | Climatic modes influencing annual rainfall in Debundscha and neighbouring stations based on PLSR- VIP scores. [Colour figure can

📷 be viewed at wileyonlinelibrary.com]

61.65%, 42.14% to 70.36%, and 13.94% to 37.94% respectively, for Idenau, Debundscha, Limbe, Likomba, Tole, and Meanja (not shown). The total percentage of variance explained for the pre- monsoon season is above 50% in most of the stations including Debundscha (56.64%). The highest variance is observed in Tole during the post- monsoon season (70.36%) while the lowest is for annual rainfall in Meanja (13.94%). This suggests that Meanja is the station with the lowest percentage of variance explained by the climatic modes while Tole has the highest percentage of variance explained by the climatic modes (not shown).

4 | Discussion

4.1 | Spatio- Temporal Distribution and Long- Term Trends in Rainfall

Analysis shows that there has been a steady decline in rainfall in Debundscha and most of the neighbouring stations except Limbe. This is consistent with results from other studies revealing declining trends in annual and seasonal rainfall around the study area and in parts of Gulf of Guinea Basin (Nkiaka 2022; Nkiaka and Okafor 2024). Results from the analysis also show that whilst highest rainfall in Limbe, Likomba and Meanja is recorded in the month of July, in Debundscha, Idenau and Tole, the highest rainfall is recorded in the month of August. This discrepancy may be attributed to the spatial and temporal distribution of maximum rainfall during the monsoon season in the region. It may also be attributed to the fact that different climatic modes control seasonal rainfall in each station which could potentially influence the spatial and early or late arrival of peak rainfall in each station. Analysis from the study also showed that whilst annual rainfall has been declining in Debundscha and neighbouring stations, Limbe is witnessing statistically significant increasing trends in seasonal and annual rainfall. This is in- line with analysis from other studies reporting of increasing trends in annual and seasonal rainfall in Limbe (Findi et al. 2022; Tume 2022). However, additional studies are needed to identify the factors driving rainfall increase in Limbe considering that the rest of neighbouring areas are experiencing a decline.

FIGURE 6 | Climatic modes influencing seasonal rainfall in Debundscha and neighbouring stations based on PLSR- VIP scores. [Colour figure

📷 can be viewed at wileyonlinelibrary.com]

The increasing rainfall in Limbe has contributed to natural disasters leading to a surge in floods and landslides events in the city (Findi et al. 2022; Enomah et al. 2023). These extreme weather events pose serious risks to communities and infrastructure, exacerbating existing vulnerabilities in the region. Considering that the present study focused on annual and monthly timescales, there is a need for additional studies at daily and sub- daily timescales as such timescales are more relevant for building resilience to hydrometeorological risk. Additional studies are also needed to identify areas that are prone to hydrometeorological disasters in the area. To strengthen the resilience of the population to hydrometeorological disasters, there is a need to adopt climate information services that can provide tailored climate information to mitigate disaster risks in real- time (Nkiaka et al. 2020). Whilst increasing rainfall is contributing to natural disasters in Limbe, declining rainfall in the other localities also has profound implications on water availability given the increasing cases of water scarcity in the region (Nkiaka 2022; Fonjong and Zama 2023). Therefore, it is crucial to implement local adaptation measures, such as rainwater harvesting, which has proven effective in reducing water scarcity in certain communities within the region (Adamu et al. 2020; Mbua et al. 2024).

The impact of declining rainfall on crop productivity also has a profound impact on socio- economic activities in the study area. For example, it has been reported that crop yields exert a significant impact on educational attainment because parents utilise income from crop sales to purchase their children's textbooks, uniforms, and other school supplies and pay tuition fee (Fuller et al. 2018). Therefore, a decline in crop yield could potentially lead to reduced income for parents thereby forcing them to spend less on their children's education, which may lead to a fall in the overall educational attainment in the area (Fuller et al. 2018). A decline in rainfall in the region has also led to a substantial decline in the yield of major tradables (rubber, oil palm, and banana) for large- scale agribusinesses operating in the area (Kimengsi and Muluh 2013). Such declines in major tradables could lead to a decline in the quantity of exported goods and reduce the balance of payments for the country with wider negative socio- economic consequences. Considering that the region is an important agricultural zone, it is essential for farmers in the region to adopt climate- smart agricultural practices and climate information services. These measures have already demonstrated their effectiveness in helping farmers in other parts of Africa reduce the negative impacts of climate change on crop yields (Amegnaglo et al. 2017; Mutenje et al. 2019).

4.2 | Climatic Modes Associated With Rainfall Variability

The different climatic modes influencing annual and seasonal rainfall in Debundscha and neighbouring stations are analysed using the PLSR. The present study has established that the dominant climatic modes associated with rainfall in Debundscha are AMO and GSST. In the monsoon season, the dominant climatic modes associated with rainfall in Debundscha are AMO, SOI, GSST, and MEI. This suggests that a decline in annual and seasonal rainfall in Debundscha may partly be attributed to the above climatic modes. AMO has been shown to influence decadal rainfall variability in several regions of Africa (Lüdecke et al. 2021). Pre- monsoon rainfall in Debundscha also shows a significant decline at the 5%. Whilst many climatic modes influence pre- monsoon rainfall in Debundscha, ENSO3.4 is the dominant climatic mode associated with rainfall distribution during this season. Other studies in the region have also revealed the strong role of ENSO3.4 on pre- monsoon rainfall (Emmanuel 2022; Nana et al. 2024). Given that this is the first study to report on the climatic modes influencing rainfall in Debundscha, additional studies are needed to shed more light on the physical mechanisms through which these climatic modes influence rainfall in Debundscha. Such analysis could help explain the reasons for the decline in rainfall in the area.

Analysis also shows that SOI is the dominant climatic mode influencing annual rainfall in most of the stations in the area. This is in line with analyses from other studies in the region that have reported the dominant role of ENSO on rainfall distribution in the Gulf of Guinea (Emmanuel 2022; Lüdecke et al. 2021; Nana et al. 2024). In addition, SOI has also been shown to be associated with rainfall variability in the coastal areas of Nigeria which share the same coastline with Debundscha and neighbouring stations (Egbuawa et al. 2017). Considering that Limbe is the only station around Debundscha to record a statistically significant increase in annual and monsoon rainfall, statistical analysis revealed that the dominant climatic modes associated with annual and seasonal rainfall in Limbe include the MEI and GSST with the latter playing a greater role in rainfall variability across most seasons. The results are consistent with those from another study in the Gulf of Guinea showing the dominant role of the GSST on the temporal and spatial distribution of rainfall in the region (Ideki and Lupo 2024). IODWP was also found to influence pre- monsoon and monsoon rainfall in Limbe which is also consistent with results from other studies showing the role of Indian Ocean Dipole on seasonal rainfall variability in the region (Moihamette et al. 2024; Tanessong et al. 2024).

For the first time, the dominant climatic modes associated with rainfall in Debundscha, which is one of the rainiest places on Earth, are revealed. Considering that the percentage of variance explained by the different climatic modes is low in some stations, this suggests that there may be other factors associated with rainfall in the region. Therefore, additional studies are needed to unearth other factors influencing rainfall in Debundscha and other stations. Nevertheless, the results presented in this study may be used by stakeholders to provide seasonal and sub- seasonal climate forecasts in the region to mitigate hydrometeorological risk, enhance water resources management, and boost agricultural productivity.

5 | Conclusion

The objectives of this study were to analyse trends in seasonal and annual rainfall and determine the key climatic modes influencing seasonal and annual rainfall in Debundscha and neighbouring localities. Statistical analyses show consistent declining trends in annual and seasonal rainfall in Debundscha and the rest of the stations, except Limbe, which exhibits a consistent increase in annual and seasonal rainfall. Whilst the magnitude of annual rainfall declines in Idenau and Debundscha appear to be relatively high, they are statistically non- significant. At the same time, the increasing trends in Limbe are statistically significant at the annual and seasonal timescales. However, additional investigations are needed to explain why Limbe is the only station experiencing a statistically significant increase in annual and seasonal rainfall in the study. Further analysis using the PLSR revealed that AMO, SOI, IODWP, GSST, MEI, and ENSO3.4 have a stronger influence on annual and seasonal rainfall in several stations in the region. However, different sets of climatic modes are responsible for rainfall in each station. Analysis from this study has revealed for the first time the dominant climatic modes influencing seasonal and annual rainfall in Debundscha and neighbouring stations. This information will be invaluable as it might limit the subjectivity around selecting the key climate modes influencing annual and seasonal rainfall in the area. However, as this study mainly focuses on identifying the key climatic modes influencing rainfall, future studies may seek to investigate the main physical mechanisms through which the different climatic modes influence annual and seasonal rainfall in the region. The methodology employed in this paper may also be used to reveal the key climatic modes associated with annual and seasonal rainfall in other regions.

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