1 Introduction

West African rainfall distribution is critical for many activities such as water resources monitoring, rain-fed agriculture, and drought and flood forecasting over the region. Many critical impact assessments require higher-order daily rainfall statistics rather than mean climatology of precipitation (Parry et al. 2007). Such statistics can elucidate extreme precipitation characteristics such as drought and flooding that have a tremendous impact on human society and the environment. Systems that are particularly vulnerable include agriculture, ecosystems, coastal areas, and water resources. Characterizing daily rainfall features is thus a key issue for an accurate assessment of climate change projections over West Africa for a wide range of decision makers. This requires a careful evaluation of the model’s performance in simulating the fine-scale hydroclimatic variables at the daily time-scale and lends to improvement of the use of regional climate models (RCMs).

Substantial efforts have been devoted to the simulation and the understanding of daily precipitation characteristics and extremes worldwide (e.g., Meehl et al. 2007; Tolika et al. 2008; Walker and Diffenbaugh 2009; Marengo et al. 2009; Liu et al. 2010; Wehner et al. 2010; Torma et al. 2011; Im et al. 2011; Crétat et al. 2013; Diallo et al. 2013a). Despite the recent progress, only few studies investigated daily precipitation behavior over the West African domain (Ibrahim et al. 2011; Sylla et al. 2013a). In fact, most of the previous studies have focused on mean precipitation climatology, examining in particular the ability of RCMs to improve the representation of the West African monsoon climatology (Afiesimama et al. 2006; Kamga and Buscarlet 2006; Sylla et al. 2009, 2010a) and its variability (Paeth et al. 2005; Hagos and Cook 2007; Sylla et al. 2010b), the understanding of the interactions between the different dynamical features affecting the monsoon (Moufouma-Okia and Rowell 2010; Sylla et al. 2011; Browne and Sylla 2012), and the study of the influence of land use changes, fine-scale topography, monsoon circulation, and ENSO dynamics on future climate (Paeth et al. 2009; Sylla et al. 2010c, 2012; Mariotti et al. 2011; Abiodun et al. 2012; Zaroug et al. 2013).

Over the region, extensive studies of daily rainfall characteristics and extreme events have been limited because the spatial distribution of rain gauges is not adequate enough to allow the description of local, regional, and large-scale hydrological phenomena (Love et al. 2004; Ali et al. 2005). On the other hand, the various merged satellite gauge daily products available exhibit substantial systematic differences, thus providing quite different representations of daily precipitation behavior (Sylla et al. 2013a). In addition to the factors mentioned above that prevent a rigorous description of daily precipitation and extremes over West Africa, none of the studies analyzed the performance of multiple RCMs using a coordinated framework that would help to improve the characterization of uncertainties related to the simulation of daily-scale precipitation and extreme events (Giorgi et al. 2008). Recently, West African Monsoon Modelling and Evaluation (Druyan et al. 2010), African Multidisciplinary Monsoon Analysis-Model Intercomparison Project, (Hourdin et al. 2010), and Coordinated Regional Downscaling Experiments (CORDEX; Giorgi et al. 2009) proposed the inception of such framework.

The CORDEX program aims to provide high-resolution regional climate change projections using a wide range of greenhouse gas (GHG) emission scenarios to support impact assessment and decision making at the regional level. The first set of present-day CORDEX simulations using ERA-Interim reanalysis at the boundaries has been analyzed in detail by Nikulin et al. (2012), which they focused mostly on precipitation climatology. They concluded that the multi-model ensemble mean generally outperforms any individual RCM simulation in line with previous studies (Druyan et al. 2010; Paeth et al. 2011; Diallo et al. 2012, 2013b; Gbobaniyi et al. 2013; Sylla et al. 2013b). In this paper, we extend the study of Nikulin et al. (2012) and examine whether their conclusion could be applied to the simulation of daily rainfall characteristics over West Africa by considering a wide range of estimates in the higher-order statistics such as the simple daily intensity index, the frequency of wet days and heavy rainfall events, the 95th percentile, and maximum dry and wet spells relevant for agricultural impact studies over the region.

In Section 2, we describe the regional climate models, simulation designs, and methods. Section 3 discusses the results, and the main conclusions are summarized in Section 4.

2 Data and methods

In this study, an ensemble of ten RCMs participating to the CORDEX-Africa program is analyzed and intercompared. The list of the RCMs, their institutions, precipitation schemes (convection and micro-physics), and references are presented in Table 1. The RCMs are integrated over the whole African domain continuously for the entire 20-year period from January 1989 through December 2008 at a spatial resolution of 50 km (~0.44°), employing the ERA-Interim reanalysis (Dee et al. 2011) at the lateral boundaries and for initialization. The simulations are evaluated over the West African domain of Fig. 1, which exhibits some localized highlands around Cameroon (Cameroon Mountains), Central Nigeria (Jos Plateau), and Guinea (Guinea Highlands).

Table 1 Participating RCMs including their dynamics and physical parameterization (from Nikulin et al. 2012)
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Fig. 1
figure 1

The West African domain and topography (in meters) with the highlands highlighted by their respective acronyms: Guinea Highlands (GH), Jos Plateau (JP), Cameroon Mountains (CM) and Sudanese Highlands (SH). The two boxes indicate the two sub-regions, i.e., Sahel and Guinea, selected for quantitative analysis

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To assess the different CORDEX RCMs at the daily time-scale, a set of observation datasets can be used. For example, Sylla et al. (2013a) examined a number of observed daily rainfall products available over Africa, including the 1° × 1° Global Precipitation Climatology Project (GPCP 1DD; Huffman et al. 2001) and the 0.25 ° × 0.25 ° resolution Tropical Rainfall Measuring Mission (TRMM; Kummerow et al. 2001; Huffman et al. 2007), and found that they exhibit substantial systematic differences in mean rainfall and especially in frequency of wet days, intensity, and extremes as well as maximum length of wet and dry spells. However, the GPCP 1DD shows much better consistency with other observations and produces values within the range of the other observations (Sylla et al. 2013a). Recently, Giorgi et al. (2014) compared both GPCP 1DD and TRMM to their regional climate model simulations driven by two global climate models (GCMs) and found that the precipitation intensity distributions from the GCMs are close to the GPCP data, while the RegCM4 ones are closer to TRMM. To account for these uncertainties, we then employ both TRMM and GPCP that are satellite-derived datasets merged with rain gauges and respectively available from late 1996 and 1997 to present.

Our analysis is carried out by considering only the common period across the observation and the simulations (1997–2008) and during the West African Monsoon summer season (June–September: JJAS). After a brief assessment of mean precipitation, we focus on the different hydroclimatic indices (summarized in Table 2) to assess the characteristics of daily rainfall (simple daily intensity index, frequency of wet days and heavy rainfall events, extreme precipitation events represented as the 95th percentile, and maximum wet spell and dry spell length) and to what extent the multi-model ensemble mean of the indices outperforms the individual RCM members. We define a rainfall event as a day with minimum precipitation amount exceeding 1 mm. The multi-model ensemble means are derived after calculating the indices for each of the individual RCMs.

Table 2 Selected indices and their definition
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Further analysis on the RCMs’ performance considered are the mean bias (MB), which is defined as the difference between the area averaged value of the simulation and both the GPCP and TRMM, and the pattern correlation coefficient (PCC) that indicates how well the observed pattern is captured. These quantitative measures are done by considering only the grid points over the land for all the indices defined in Table 2. The calculation is performed for the West African study domain and the two homogeneous climate sub-regions, i.e., Sahel and Guinea, which are selected based on a previous study by Gbobaniyi et al. (2013). In fact, Gbobaniyi et al. (2013) found that the majority of CORDEX RCMs simulate fairly well the mean seasonal cycle as well as the interannual variability of rainfall over both sub-regions.

It is worth mentioning that for this analysis, only GPCP is used in the spatial distribution to have an immediate idea about the spatial patterns; however, for the quantitative measures of model performance (Tables 3, 4, and 5), both GPCP and TRMM are employed to account for uncertainties in the observed daily precipitation products.

Table 3 Mean bias (MB) and pattern correlation coefficient (PCC) between simulated precipitation intensity and that of the observations (GPCP and TRMM)
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Table 4 Mean bias (MB) and pattern correlation coefficient (PCC) between simulated frequency and that of the observations (GPCP and TRMM)
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Table 5 Mean bias (MB) and pattern correlation coefficient (PCC) between simulated extreme precipitation at 95th percentile and that of the observations (GPCP and TRMM)
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3 Results and discussion

3.1 Rainfall climatology

Before evaluating and intercomparing the characteristics of daily rainfall events from the different RCMs, it is helpful to assess the mean summer monsoon climatology (JJAS). The spatial distribution of mean JJAS daily rainfall over West Africa is shown in Fig. 2 for GPCP (Fig. 2a), the ensemble mean (Fig. 2b), and each of the RCM members (Fig. 2c, l). Simulated rainfall climatologies are overall consistent with GPCP observation. The RCMs generally capture the spatial variability of rainfall with high amount along the intertropical convergence zone (ITCZ) situated around 10° N and maxima well represented over the complex terrains of Guinea Highlands, Jos Plateau, and Cameroon Mountains. A number of differences can be found across the different simulations with regard to the magnitude and spatial extent of the mean rainfall. CNRM-ARPEGE and CCLMcom-CCLM largely underestimate the rainfall intensity along the ITCZ; UCT-PRECIS exhibits weaker maxima over mountainous regions, while UQAM-CRCM, DMI-HIRHAM, UC-WRF, and MPI-REMO consistently simulate too much rainfall amount off-shore of Guinea Highlands. As also seen in Nikulin et al. (2012) and Gbobaniyi et al. (2013), the multi-model ensemble outperforms the individual RCM members. However, whether this can be expected for the higher-order statistics remains to be seen. We thus investigate at what extent the individual RCMs along with their ensemble mean are able to reproduce the mean intensity and frequency of total rainfall, heavy rainfall events, their 95th percentile, and dry and wet spells relevant for agricultural impact assessment over West Africa.

Fig. 2
figure 2

Mean JJAS rainfall climatology (mm/day) from GPCP observation, each of the CORDEX models and their multi-model ensemble over West Africa for the period JJAS 1997–2008

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3.2 Intensity and frequency of daily rainfall events

The spatial distribution of the simple daily intensity index, which is calculated as the mean intensity of rainfall events, is presented in Fig. 3 for GPCP, the RCMs ensemble mean, and the individual models. Table 3 summarizes the MB and the PCC between simulated and observed rainfall intensities from both GPCP and TRMM calculated for the whole West African region and also for the Gulf of Guinea as well as Sahel sub-regions. For the spatial distribution, the GPCP data show higher rainfall intensities over the Sahel region, the Guinea Highlands, and off the west coast. Striking differences are found between the RCMs and the observed precipitation estimates on one hand and, on the other hand, across the RCMs themselves in terms of rainfall intensity and their spatial distribution. For instance, the RCMs employing Tiedtke (1989) as convection scheme (except KNMI-RACMO) tend to produce greater maxima in the Sahel and/or the orographic regions. The other RCMs using different versions of the Kain convective scheme (Kain and Fritsch 1990, 1993; Kain 2004) fail to extend the high intensities northward. However, a common feature among the RCMs is the underestimation of the simple daily index over the Sahel and along the Guinea Coast (except for ICTP-REGCM, DMI-HIRHAM, and CCLMcom-CCLM that exhibit overestimation in the Sahel). This underestimation leads to strong biases over the different sub-regions and to relatively low PCC between RCMs and GPCP observations (Table 3). The bias is even greater and the PCC even lower when compared to TRMM because the latter produces more intense precipitation events than GPCP (Sylla et al. 2013a; Giorgi et al. 2014). For instance, UQAM-CRCM, UC-WRF, and MPI-REMO shift the higher intensities in regions equatorward inland and overestimate the rainfall intensity in Oceanic region off the west coast, resulting in a dry bias over the Sahel of up to 18 and 48 % and a low PCC not exceeding 0.72 and 0.31 when compared with GPCP and TRMM, respectively. Likewise, SMHI-RCA, UCT-PRECIS, and KNMI-RACMO produce much lower precipitation intensities during wet days over the whole West Africa, thus generating a considerable mean bias of about 35 % (45 %) and a PCC mostly between 0.49 and 0.76 (0.37 and 0.43) with respect to GPCP (TRMM). However, the most deficient RCM is CNRM-ARPEGE that drastically underestimates the intensity with respect to both observation products across the different sub-regions and also over the entire West Africa, leading to a negative bias of up to 60 % in the Sahel, 59 % in the Guinea region, and 62 % in West Africa. Therefore, Fig. 3 and Table 3 indicate that the individual model biases in the intensity of rainfall events are negative in almost all RCMs but vary substantially in magnitude across the RCMs and from region to region. With regards to these results, it is thus evident that the ensemble mean fails to improve from all of its individual RCM members (except for the West Africa PCC), although a few of them are significantly improved.

Fig. 3
figure 3

Mean intensity of daily rainfall events (mm/day) from GPCP observation, each of the CORDEX models and their multi-model ensemble over West Africa for the period JJAS 1997–2008

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Despite this poor performance, a good agreement is found among the RCMs and the observations with regard to the spatial distribution of the frequency of rainy days (Fig. 4), with a PCC exceeding 0.8 over the Sahel and whole West Africa (Table 4). In fact, they all place the greater number of events along the ITCZ and over orographic regions with a decreasing gradient north and south of the band of maximum events. It should be emphasized that PCCs are lower with respect to both observations in the Guinea region compared to those calculated in the Sahel and the whole West Africa. Such RCM behavior is probably due to the presence of fine-scale precipitation features simulated in regions of steep topography. Despite the good agreement between the simulated and observed pattern of precipitation frequency, prominent differences among the magnitudes are present (i.e., Table 4). For instance, the RCMs employing different versions of Kain convective schemes (Kain and Fritsch 1990, 1993; Kain 2004) overestimate the frequency of rainfall events in the Guinea regions, while those using Tiedtke (1989) scheme simulate less frequent rainfall events in the Sahel. Specifically, UCT-PRECIS, KNMI-RACMO, UC-WRF, and CNRM-ARPEGE produce frequency values that are consistently and considerably larger than observations and other RCMs mostly in the Guinea regions with a mean bias varying between 31 and 54 % when compared to GPCP and TRMM (i.e., Table 4); SMHI-RCA shifts the events further south, resulting in a positive bias of about 10 % in the Guinea region and a negative bias of 15 % in the Sahel with respect to GPCP, respectively. The CCLMcom-CCLM exhibits a narrower band confined along 10 N, causing thus a general underestimation of precipitation events reaching 33 % in the Sahel, 13 % in the Gulf of Guinea, and 22 % in West Africa. However, when compared to TRMM, both CCLMcom-CCLM and SMHI-RCA exhibit respectively positive bias in the Gulf of Guinea, the Sahel, and the whole West Africa, thus confirming uncertainties present in the daily precipitation products over West Africa (Diallo et al. 2012; Nikulin et al. 2012; Sylla et al. 2013a).

Fig. 4
figure 4

Mean frequency of wet days (expressed in percent of total annual days) from GPCP observation, each of the CORDEX models and their multi-model ensemble over West Africa for the period JJAS 1997–2008

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Figures 3 and 4 and Tables 3 and 4 clearly show that the RCMs provide a substantially different description of the daily characteristics of the summer monsoon precipitation. In particular, UQAM-CRCM, DMI-HIRHAM, and MPI-REMO simulate rainfall events in line with observations (less than 15 % of bias) and excessive rainfall intensity over oceanic regions off the west coast, which contributes to the greater mean precipitation amounts in these areas. UCT-PRECIS, KNMI-RACMO, UC-WRF, and CNRM-ARPEGE produce much more frequent events and less intensity than observations in the Guinea regions, contributing to the lower and moderate precipitation amounts. ICTP-REGCM, SMHI-RCA, DMI-HIRHAM, and CCLMcom-CCLM exhibit the most reasonable intensity among the RCMs over West Africa but different bias magnitudes in the frequency and mean precipitation climatology.

Overall, this analysis reveals that the RCMs differ noticeably in terms of intensity and frequency of the rainfall events, thus providing a wide range of systematic bias in mean precipitation climatology. This indicates that they do not simply receive bias from the common boundary conditions ERA-Interim and that the differences mainly arise from their various internal dynamical and physical schemes. For example, CNRM-ARPEGE employing Bougeault (1985) as a convective scheme produces less intense but more frequent rainfall events domain-wide. Kain convective schemes (Kain and Fritsch 1990, 1993; Kain 2004) show a similar behavior than Bougeault (1985) but only in the Gulf of Guinea. In contrast, Tiedtke tends to exhibit different performances across the different RCMs probably because of the combination of different cloud microphysics/large-scale precipitation schemes. Such behavior of RCMs highlights the existence of large uncertainties associated with the simulation of daily rainfall characteristics, illustrating the importance of carefully validating RCMs over West Africa. The ensemble mean shows a good performance but exhibits mostly the lowest bias and highest PCC only with regards to the frequency of precipitation events (Table 4). It is worth noting that this good performance results from cancellation of errors of opposite sign.

3.3 Daily extreme rainfall events

The extreme measures are characterized as the heavy rainfall events, the 95th percentile of daily rainfall events, and the maximum dry and wet spell lengths in the monsoon season. Observed and simulated numbers of heavy rainfall events are displayed in Fig. 5. Generally, all the RCMs (except ICTP-REGCM, DMI-HIRHAM, and MPI-REMO) along with the ensemble mean produce a much lower frequency of heavy rainfall events over land. A number of considerations can be highlighted here. GPCP, which is showing a greater number of wet days along the ITCZ and higher intensity north of it, indicates that the heavy rainfall events are located in the Sahel region rather than the Gulf of Guinea during the monsoon period. These events are associated with the occurrence of deep convection events (Diedhiou et al. 1998; Redelsperger et al. 2002; Sylla et al. 2013b). In addition, the excessive rainfall intensity in oceanic region off the west coast shown by UQAM-CRCM, DMI-HIRHAM, UC-WRF, and MPI-REMO is likely due to the considerable overestimation of the number of heavy rainfall events simulated there. Furthermore, UCT-PRECIS, KNMI-RACMO, UC-WRF, and CNRM-ARPEGE underestimate the rainfall intensity over land due to a lower number of heavy precipitation events. Finally, ICTP-REGCM, DMI-HIRHAM, and MPI-REMO produce a number of heavy rainfall events consistent with observations over the Sahel and Guinea region as a consequence of moderate rainfall intensity. The results indicate that the RCMs employing Tiedtke (1989) scheme exhibit different magnitudes with DMI-HIRHAM and MPI-REMO, producing a greater number of heavy rainfall events than CCLMcom-CCLM and KNMI-RACMO. In addition, the RCMs using Kain convective schemes but also Bougeault (1985) and Gregory and Rowntree (1990) show deficiencies in simulating the heavy rainfall in the Sahel during the monsoon season. This variety should strongly impact the occurrence and spatial distribution of the simulated extreme rainfall events across the RCMs over West Africa.

Fig. 5
figure 5

Mean frequency of heavy rainfall events (expressed in percent of total annual days) from GPCP observation, each of the CORDEX models and their multi-model ensemble over West Africa for the period JJAS 1997–2008

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The extreme rainfall event that is based on the 95th percentile shown in Fig. 6 essentially follows the same spatial pattern of daily precipitation intensity. Most of the RCMs consistently exhibit greater estimates than GPCP over the coastal areas of Guinea Highlands and off-shore and along the ITCZ. However, CCLMcom-CCLM, UCT-PRECIS, KNMI-RACMO, and CNRM-ARPEGE tend to produce generally a lower 95th percentile over the entire domain as a consequence of a lower number of heavy precipitation events and lower intensity as discussed earlier. Such underestimation leads to a strong negative bias of up to 70 % in the Sahel and 58 % in the Guinea region with respect to GPCP and to a much stronger negative bias with respect to TRMM (i.e., Table 5). In addition, UQAM-CRCM, SMHI-RCA, and UC-WRF simulate a narrower band of maximum 95th percentile shifted southward in the Guinea region, resulting in wetter extreme of up to 13 % in this region. It should be noted that UC-WRF and UQAM-CRCM revert the sign of the bias when compared to TRMM in the Guinea and West Africa regions, respectively, illustrating the existence of more intense precipitation events in this observed products as discussed above. Finally, DMI-HIRHAM, ICTP-REGCM, and MPI-REMO reveal a broader area centered around the ITCZ where the 95th percentile exceed the observed ones, resulting in various signs and magnitudes of the bias. For most RCMs, the PCC with respect to both observations (Table 5) exceeds 0.7 in the Sahel and over West Africa but shows lower values (in fact much lower) when compared to TRMM, i.e., between 0.3 and 0.6 in Guinea region, thus highlighting difficulties of the RCMs and observations to capture consistent fine-scale features in regions of steep topography. Through error cancellation, the ensemble mean exhibits an accurate estimate of the extreme rainfall index and substantially improves on most of the individual RCM members, mainly over the Guinea region, and provides patterns and values of 95th percentile estimates closer to those of the GPCP and TRMM observations.

Fig. 6
figure 6

Mean 95th percentile of daily rainfall events (mm/day) from GPCP observation, each of the CORDEX models and their multi-model ensemble over West Africa for the period JJAS 1997–2008

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Other important aspects of daily precipitation characteristics for evaluating climate variability and change impacts is the maximum length of wet and dry spells within the monsoon season. Figure 7 shows the average maximum wet spell length for GPCP observation, the ensemble mean, and each of the RCM members during JJAS. For regional statistics measure of this index with respect to both GPCP and TRMM, see supplemental materials. The spatial distribution is broadly consistent with the number of wet days shown in Fig. 4. GPCP shows that the wet spell length over West Africa is mostly between 3 and 10 % of total annual days except over orographic regions where they reach more than 20 %. Half of the RCMs (SMHI-RCA, UQAM-CRCM, DMI-HIRHAM, MPI-REMO, and CCLMcom-CCLM) produce similar lengths of maximum consecutive wet days as GPCP but with different spatial extents. However, UCT-PRECIS, KNMI-RACMO, UC-WRF, CNRM-ARPEGE, and ICTP-REGCM simulate generally longer than observed wet spells over the entire West Africa and, in particular, over orographic regions. This result is an indication of the persistent orographic forcing in these RCMs, with their different convective schemes tending to maintain and extend precipitation over mountainous areas once convection is activated (Sylla et al. 2013a). This behavior of the different models leads to an overall failure of the multi-model ensemble mean to outperform the individual RCM members as a positive bias still prevails along the ITCZ and over complex terrains, consistent with the higher MB and lower PCC (table not shown for brevity, see supplemental materials) over both sub-regions and the entire West Africa.

Fig. 7
figure 7

Mean maximum wet spell length (expressed in percent of total annual days) from GPCP observation, each of the CORDEX models and their multi-model ensemble over West Africa for the period JJAS 1997–2008

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The spatial patterns of the average maximum consecutive dry days or dry spell length (Fig. 8) show a good general agreement across the different RCMs, their ensemble mean, and GPCP observation. As expected, the shortest dry spell length is found along the zone of maximum precipitation during the summer monsoon season and regions of complex topography and the longest dry spells to the south and north of it. The RCMs reproduce realistic patterns of the maximum dry spell length, highlighted by a PCC of up to 0.9 (table not shown for brevity, see supplemental materials), although a few short-comings are still evident. Differences between the RCMs are likely tied to the difference in the models’ internal dynamics and physics (Nikulin et al. 2012; Kalognomou et al. 2013). For instance, DMI-HIRHAM, MPI-REMO, and CCLMcom-CCLM employing Tiedtke (1989) convective scheme overestimate the spells over the Sahel and flatter terrains of Guinea region, while UCT-PRECIS, UC-WRF, and CNRM-ARPEGE employing other convection schemes shift the shortest dry spells further north because of more frequent rainfall events and longer maximum wet spell length in these areas. As a consequence, the ensemble mean provides mixed benefits.

Fig. 8
figure 8

Same as Fig. 7 but for the mean maximum dry spell lengths

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4 Summary and conclusions

Characterizing daily rainfall events for assessing and understanding climate change over Africa is a challenging problem. Simulating and generating robust precipitation change at the daily time-scale is a critical issue for many applications over the region such as water resources, agriculture, drought, and flood monitoring. Therefore, in this paper, we present an assessment and intercomparison of ten different RCMs within the framework of the CORDEX in the context of the multi-model ensemble mean. The performance of the individual RCM members and their ensemble mean is examined in detail for various aspects of West Africa monsoon (WAM) daily precipitation. We focused not only on mean precipitation climatology but also on the characteristics of daily rainfall events such as intensity, frequency, and extremes as well as mean maximum length of dry and wet spells within the peak of the monsoon season, i.e., JJAS.

Our analysis reveals that considerable discrepancies exist between the RCMs and the observation on one hand and among the different RCMs themselves on the other. In particular, the simulations differ substantially in terms of mean precipitation climatology and higher-order daily statistics such as intensity of rainy days, frequency, extremes, and duration of rainfall events during the WAM period and mostly driven by their convection schemes. Compared to GPCP and TRMM, some of the individual models (UQAM-CRCM, DMI-HIRHAM, and MPI-REMO) clearly produce frequency of wet days similar to observations but much more intensity in oceanic regions off the west coast adjacent to the Guinea Highlands, resulting from the simulation of a larger number of heavy precipitation events indicating more intense extremes. RCMs such as UCT-PRECIS, KNMI-RACMO, UC-WRF, and CNRM-ARPEGE exhibit a higher number of wet days but much less intensity over West Africa. This is mainly due to the occurrence of fewer frequent heavy rainfall events contributing to the simulation of less severe extremes and longer wet spells. Overall, the RCMs analyzed here provide quite different descriptions of daily rainfall features and thus highlight the existence of large uncertainties in the simulation of daily precipitation characteristics. In fact, Crétat et al. (2013) argue that a model’s weaknesses to accurately simulate the daily rainfall over Africa may be tied to physical parameterization deficiencies. The multi-model ensemble outperforms most of the individual RCM members in terms of mean precipitation climatology, intensity, and frequency of wet days and to a lesser extent the 95th percentile and mean dry and wet spell duration through cancellation of opposite-signed bias of similar magnitude. Although it cannot be generalized, such an approach produces encouraging results and can help, to some extent, to improve the robustness of the response of the West African Monsoon daily precipitation to the anthropogenic GHG warming.