# Revealing the spatiotemporal precipitation patterns of ECMWF fifth-generation reanalyses since the mid-20th century in West-Africa

René Bodjrènou, Luc Ollivier Sintondji, Françoise Comandan

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René Bodjrènou, Luc Ollivier Sintondji, Françoise Comandan. Revealing the spatiotemporal precipitation patterns of ECMWF fifth-generation reanalyses since the mid-20th century in West-Africa. Environmental Advances, 2025, 20, pp.100636. 10.1016/j.envadv.2025.100636. hal-05047268

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Distributed under a Creative Commons Attribution 4.0 International License
Revealing the spatiotemporal precipitation patterns of ECMWF
fifth-generation reanalyses since the mid-20th century in West-Africa

René Bodjrènou<sup>a,b,*</sup> <sup>●</sup>, Luc Ollivier Sintondji<sup>b,c</sup>, Françoise Comandan<sup>c</sup>

ARTICLE INFO

Keywords:

Extreme precipitation

SNP method

IDW method

ERA5

trend

A B S T R A C T

Reanalysis datasets are a viable alternative to ensure the continuity of hydrological studies and to assess climate variability in regions where the availability and/or quality of observational data is limited. This study revealed the performance of the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses, namely ERA5 (0.25°x0.25°) and ERA5-Land (0.1°x0.1°), over West Africa. They were analyzed on spatial and temporal scales (annual, monthly, daily, and hourly). The reanalysis time series were obtained by Selecting the Nearest Pixel (SNP) closest to the point station or by the Inverse Distance Weighted (IDW) method and compared with the observational data using Pearson correlation (c) and Relative Mean Absolute Error (RMAE). The results showed on average similar performance between ERA5 and ERA5-Land reanalyses, and also between SNP and IDW methods. However, a significant difference can be observed at some stations/areas due to the influence of topography and wind. Both reanalyses performed well on a spatial scale, with a clear distinction between the wet and dry areas. They also performed well on an annual (c = 0.60 and RMAE=22 %) and monthly (c = 0.80 and RMAE=40 %) scales. For 713 point stations. ERA5 and ERA5-Land showed negative trends in interannual variability for 82 % and 94 %, respectively. They agreed with the trends derived from observations (negative trends for 96 % of stations). However, the reanalyses performed poorly on both daily (c = 0.21 and RMAE=128 %) and hourly (c = 0.06 and RMAE=170 %) scales. Maximum daily precipitation (RX1day) is also less well represented, sometimes with negative correlations. The ECMWF fifth generation reanalyses need to be adjusted to improve their performance in describing precipitation.

# 1. Introduction

Precipitation patterns vary widely across the planet. Some places receive more precipitation than others within the same continent, region or sub-region, sometimes with more intense and frequent extreme precipitation events (Cucchi et al., 2020; Alexander, 2015). The African continent is experiencing an increase in extreme precipitation events, with significant impacts on agriculture and damage to homes and personal property (McBride et al., 2022; Bolan et al., 2024). Numerous studies also indicate that West Africa has been characterized by more pronounced droughts since the mid-20th century (Tapsoba et al., 2004; Nkrumah et al., 2019; Incoom et al., 2020). They state that the increase in extreme rainfall in the region has had many implications for economic development, as it significantly affects agriculture, the main development sector. Therefore, a better understanding of the current

precipitation variability is needed to design effective adaptation plans to
reduce the sector's vulnerability.

Observational data (in-situ measurements) are generally used to
describe these impacts. Unfortunately, certain factors can challenge
their ability to conduct in-depth analyses of climate variability and
hydrological impacts. These include poor spatial/temporal coverage and
low quality (Awange et al., 2016). According to Qian et al. (2024),
observational data have difficulty describing extreme climate events in
certain regions, such as West African countries, where there are few
monitoring stations (Galle et al., 2018). They are sometimes inconsis-
tent, inaccurate, and insufficient to represent current climate variability
and trends (Yagaso et al., 2024). Limitations in terms of spa-
tial/temporal coverage depend on the year of installation of measuring
stations and their densification, which has only become significant in
recent decades (Galle et al., 2018). Faced with poor data quality, experts

<footnote><sup>a</sup> Institut des Géosciences de l'Environnement, Université de Grenoble Alpes, CNRS, IRD, IGE, Grenoble, France</footnote>

<footnote><sup>b</sup> Centre d'Excellence d'Afrique pour l'Eau et l'Assainissement, Bénin</footnote>

<footnote><sup>c</sup> Laboratoire d'Hydraulique et de Maîtrise de l'Eau de l'Institut National de l'Eau (LHME-INE), Université d'Abomey-Calavi, Benin</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: renebodjrenou@gmail.com (R. Bodjrènou).</footnote>
have developed effective and satisfactory techniques (e.g., interpolation, filling in missing data) to facilitate the continuity of hydrological studies (Camuffo et al., 2022; Bodjrenou et al., 2024; Qian et al., 2024). However, the effectiveness of each technique depends on the percentage of missing data or the spatial distribution of the measuring stations. Thus, alternative measures are needed to ensure the continuity of hydrological studies and to assess climate variability in these areas.

Some authors have suggested using reanalysis as an alternative to fill in missing data (Sambieni et al., 2024). Others use it to correct regional model data, or even entirely in environmental impact studies and climate characterization studies (Dai et al., 2023; Cucchi et al., 2020). The certainty of the results presented by these authors depends on the accuracy and spatial resolution of the reanalysis data or models used. Depending on the size of the basin or the study area, some reanalyses may be inappropriate or unsuitable due to their coarse resolution (Bodjrenou et al., 2023; Sambieni et al., 2024). In fact, the lower the resolution of the model, the more the entire study area must be considered as a homogeneous unit. The low temporal coverage of the series (availability over a short period) may also hinder their use. Some reanalyses have only recently started production, and others have even stopped. This is the case for the WFDE5 reanalysis, whose production started in 1979 and will be discontinued after 2019 (Cucchi et al., 2020). The situation is almost the same for the Climate Forecast System Reanalysis (CFSR). These limitations often restrict the assessment of trends over a long period. On a spatial scale, it should be noted that some reanalyses do not cover the entire globe. Others do not cover West Africa, where data quality and availability are limited (Pelosi, 2023).

The fifth-generation reanalyses of the European Center for Medium-Range Weather Forecasts (ECMWF) for Global Climate and Meteorology (ERA5) address these challenges. Both versions of the ECMWF reanalyses (namely ERA5 and ERA5-Land) provide a consistent description of the meteorological dataset with high spatial and hourly resolution (Hersbach et al., 2023; Munoz Sabater, 2019). They provide the longest time series over the entire globe and overcome the difficulties associated with missing data in observation series and the analysis of precipitation trends. They have a better spatial resolution than previous ECMWF reanalyses, especially ERA-Interim, a global atmospheric reanalysis from 1979 (Hoffmann et al., 2019). This has the advantage that ERA5 can be applied over small basins. In addition, its fine temporal resolution enables the documentation of climate extremes and their impact on the hydrological cycle. This is the reason that justifies the interest in its selection in this study. In recent years, several studies have used these reanalyses for different purposes. Some authors have used them to characterize the climate of their environment under extreme climate conditions (Pepler, 2020; Qian et al., 2024; Yagaso et al., 2024). Others have used them to assess the impact of climate change on the water balance (Çaktu et al., 2022; Azam and Srivastava, 2020).

The reanalyses may have significant biases. To this end, it is necessary to assess their quality before using them (Bain et al., 2023; Jiang et al., 2021). On a global scale, the studies by Lavers et al. (2022) assessed the quality of ERA5 reanalysis data based on 24 hr precipitation observations. The authors examined a very short period (2001 to 2020), making it difficult to provide information on long-term precipitation trends. In West Africa, studies have focused on the ability of ERA5 reanalysis to simulate the water balance (Dembélé et al., 2020; Bodjrenou et al., 2023b). Very few have compared the data from this reanalysis with data from point stations (observations) in this environment. In particular, very few have explained the differences and performance of the two variants of the ERA5 reanalysis for scientists and policy-makers. Some authors have recommended the use of ERA5-Land, mainly because of its relatively better resolution than that of ERA5 and also because of the convection parameterization assumptions in the ERA5 reanalysis. Capecchi et al. (2023) pointed out that these assumptions discourage their direct use in regional or local applications. Gbode et al. (2023), limiting the comparison of ERA5 with ERA-Interim, recommend its use in studies of environmental and

hydrological impacts in West Africa. The performance of the second reanalysis variant (ERA5-Land) has not been demonstrated, and therefore, we cannot confirm whether the fifth generation reanalyses are the better ones. Furthermore, the comparison methods used by the authors often have limitations that may negatively affect the results. Yagaso et al. (2024) have explained this by the influence of topography on the qualification of reanalyses. It is therefore important to test different methods of comparison.

This study evaluates the strengths and weaknesses of precipitation from the two variants of the ERA5 reanalyses. At the same time, it examines whether they perform equally well on both a spatial and temporal scale. It also examines whether the IDW (Inverse Distance Weighted) and SNP (Single Nearest Method) methods lead to the same results.

## 2. Methods

### 2.1. Study area

The present study was conducted in West Africa, more specifically in Ivory Coast, Ghana, Togo, Benin, Nigeria and Cameroon, bordering the Atlantic Ocean, and in Guinea, Senegal, Mauritania, Mali, Burkina-Faso and Niger (Fig. 1). The study area covers a region with latitudes between 4° N and 18° N and longitudes between 14° W and 16° E. The mean annual rainfall sometimes exceeds 2,000 mm in some parts of the study area, while others record <500 mm (Nkrumah et al., 2019). The period from May to October, which corresponds to the rainy season, receives about 87 % of the precipitation, while the period from November to April, which corresponds to the dry season, receives only about 13 %.

Monthly precipitation also presents different characteristics from one region or zone to another. Some have a bimodal monthly cycle, while others have a unimodal cycle, with a peak generally observed in August (Bodjrenou et al., 2023b; Nkrumah et al., 2019). Also, the topography is irregular and sometimes characterized by the presence of very high mountains, such as the Atacora mountain, and large depressions with scattered islands (Bodjrenou and Comandan, 2023; Bodjrenou et al., 2023a). Cameroon (south-east of the study area) has the highest elevations, while Nigeria, one of its neighbors, has the lowest elevations in the center and extends southward. There are some steep areas in each country, indicating the presence of large mountains. For the countries bordering the Atlantic Ocean, we can cite "Mont Nimba", a mountain that peaks at 1,752 m in southeastern Guinea and is shared with Ivory Coast; the Atacora chain in Benin, which crosses northeastern Togo to central Ghana (Bodjrenou et al., 2023a). In Nigeria, the Chappal Waddi mountain rises to 2,419 m of altitude (Mayomi et al., 2024). It is located on the Mambila Plateau, on the border with Cameroon, and forms part of the Cameroon Line. Mount Cameroon is also the second highest peak on the African continent after Mount Kilimanjaro (Tsafack et al., 2009). Large mountains are also found in Mauritania, Mali, Burkina-Faso and Niger (Fig. 1), so it is necessary to choose effective methods for evaluating reanalyses in this environment. This can avoid overestimating or underestimating its performance.

### 2.2. Data Description

#### 2.2.1. Description of observational data

The observational data used in this study come from two sources. The first source brings together the national agencies responsible for collecting and processing meteorological data within each country (Tapsoba et al., 2004; Nkrumah et al., 2019). Except for the Cotonou station located in the sub-equatorial zone of Benin (star at lower latitude in Fig. 1), these agencies have provided daily time series over the period from 1950 to 2016. A total of 713 point stations installed before 1990 in the study area were used. Stations located near the center of each country in the study area were used as representative country stations. The raw data collected is subjected to a small amount of cleaning. Data
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Map of the study area
The stations with daily data (in round) are light blue or dark blue, depending on the year of installation, but the stations with hourly data are yellow stars. From low latitude to high latitude, we have the Cotonou and Sarmanga stations in Benin and the Bangui station in Mali. The background shows the elevation (from white for lowest to black for highest).</figcaption></figure>

with annual accumulations of <100 mm or >4,000 mm were discarded. The second data source is the AMMA-CATCH (Analyse Multidisciplinaire de la Mousson Africaine - Couplage de l'Atmosphere Tropicale et du Cycle Hydrologique) observatory. From the latter, hourly data are obtained from two stations (yellow star): one in the Sudano-Sahelian zone of Benin (Sarmanga station) and the other in Mali (Bangui station) (Galle et al., 2018). The data from these two stations, in addition to those from the Cotonou station (obtained on an hourly scale from the Meteo-Benin agency), are used to assess the hourly distribution and diurnal cycle of the reanalyses.

### 2.2.2. Description of reanalysis data

Two variants of the fifth generation of reanalyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used. These are the ERA5 and ERA5-Land reanalyses, which can be downloaded from the Copernicus website (https://cds.climate.copernicus.eu/datasets). ERA5 is the single level version, providing data from 1940 to the present day with hourly and 0.25° x 0.25° spatial resolution. Produced as part of the Copernicus Climate Change Service (S3C), the reanalysis provides a large number of meteorological variables over the entire globe. It combines model data with observations from a variety of platforms (in-situ, radiosonde, satellite) around the world to produce a comprehensive, globally consistent dataset, using the laws of physics (Hersbach et al., 2023). It is produced using a four-dimensional variational assimilation scheme (4D-VAR), which justifies its strong ability to integrate observations at the exact moment of measurement compared to other reanalyses such as ERA-Interim, MERRA2, and CFSR (Hoffmann et al., 2019; Potisomporn et al., 2023; Bodjrenou et al., 2023b).

The ERA5-Land reanalysis is the land surface version providing hourly resolution data from 1950 to the present day. It provides much improved spatial resolution, i.e., 0.10° x 0.10° (Muñoz Sabater, 2019). Like ERA5, it provides consistent time series of several climate variables over the entire globe outside the ocean zones (Bodjrenou et al., 2023a). To produce ERA5-Land, the ECMWF CHTESSEL Cy45r1 model cycle was forced with ERA5 atmospheric variables, such as air temperature and humidity. These were first corrected to reduce the altitude difference between the forcing grid and the higher resolution ERA5-Land grid.

Recognizing that uncertainties can arise from the use of a numerical model to provide a more or less accurate representation of the actual physical processes, the present study also evaluated the ERA5-Land reanalysis.

The ERA5 data were poorly assimilated for the year 2007. As a result, this year has been invalidated, and data from this period are not included in our analysis. In addition, the negative data included in the two reanalyses have been reset to zero. Dates with missing data have also been removed.

### 2.2.3. Methodology

The analysis of the spatial variability of precipitation from the two reanalyses focused on two principles. Firstly, the observation data were kriged at ERA5 resolution, which has the coarsest resolution (Hersbach et al., 2023; Muñoz Sabater, 2019). The spatial variability map of precipitation obtained with observational data was compared with that obtained with the reanalysis data. The second evaluation method was to compare data for the same date from several spatially distributed stations.

For the temporal evaluation, reanalysis time series were obtained using two methods. The first is the SNP (Single Nearest Pixel) method, which consists of selecting the pixel closest to the point station. This method is sometimes less relevant, especially when the point station is almost in the middle of four pixels. Bodjrenou et al. (2023a) used this method to assess temperature, radiation, and precipitation from reanalyses and concluded that the Atacora mountain (located at a higher latitude in the country) influences the ability of reanalyses to describe Benin's climate. Therefore, in a recent study, they deployed the IDW (Inverse Distance Weighted) method to compare hydrological variables (Bodjrenou et al., 2025). How time series are constructed with this method is well detailed in the latter studies. This method constructs a new series on the coordinate of the point station, using the weighted linear combination of the four surrounding pixels (Equation (1)).

The evaluation of reanalysis time series is based on the correlation to determine their consistency with observations. It was also based on the Relative Mean Absolute Error (RMAE), which also refers to the percentage of absolute bias. Defined by the ratio between MAE (Mean
Absolute Error) and the mean of the reference series, it varies from zero percent (best performance) to plus infinity. It is recommended to consult the article by Bodjrenou et al. (2025) for more details. The Mann-Kendall trend test, which is based on the correlation between the ranks of a time series and its order (Hamed, 2008), was used to detect trends. The reanalyses were also assessed in terms of the RX1day extreme precipitation index, defined as maximum annual rainfall (Alexander, 2015). It should be remembered that stations located in the center of each country benefited from more in-depth analyses.

$$Z_p = \frac{\sum_{i=1}^{4} \left( \frac{y_i}{D_i} \right)}{\sum_{i=1}^{4} \left( \frac{1}{D_i} \right)} \quad (1)$$

With Zp: the reanalyses data estimated in the point station

Di: the distance between pixel i and the point station

yᵢ: the reanalysis value in the pixel i

# 3. Results

## 3.1. Revealing reanalyses performance using the SNP method

### 3.1.1. Performance on interannual variability

Fig. 2 shows the interannual variability of precipitation at stations located practically in the center of each of the countries in the study area. The coordinates of these stations and the performance of the reanalysis in terms of correlation, RMAE, and trend are reported in Table 1. Analysis of Fig. 2-a shows that at stations in the Ivory Coast and Ghana, differences between reanalyses and observations in annual rainfall were greatest in the 1950s. Both reanalyses significantly underestimate annual precipitation during this decade and show an

upward trend (>0, Table 1), while the observations show high precipitation during this decade and a downward trend (−9.46 mm/yr in Ivory Coast and −4.21 mm/yr in Ghana, Table 1). We also note that the annual rainfall of the two reanalyses is almost identical at all stations, leading to almost identical correlations (mean of 0.60). The biases are also identical for both reanalyses at all stations (RMAE=22 %), except at JOS station in Nigeria. At the latter, ERA5 has a mean closer to observations, and the biases have more than doubled with ERA5-Land (RMAE=9 % for ERA5 vs. 19 % for ERA5-Land, Table 1). Mean annual precipitation at this station is 1262 mm for observations, compared with 1277 mm for ERA5 and 1673 mm for ERA5-Land. RMAE values ranged from 15 % (Siguiri in Guinea) to 38 % (Kiffa in Mauritania) for ERA5, and from 15 % (Siguiri in Guinea) to 40 % (Bouza in Niger) for ERA5-Land.

In terms of trends, observational data showed a downward trend in annual precipitation at all stations, except for Sotouboua in Togo (1.52 mm/yr for observations). The two reanalyses also show predominantly downward trends. Most of the trends are significant for both reanalyses and observations. However, there are significant differences in trend values for some stations. The largest difference between ERA5 and observations is noted at the Bouaflé station in Ivory Coast (6.44 mm/yr for ERA5 versus −9.46 mm/yr for observations, i.e., a difference of 15.9 mm/yr). Overall, ERA5-Land presents trends closer to observations, except for the Yoko station in Cameroon. For the latter, the reanalysis incorrectly simulated rainfall in 1950, leading to larger trend differences (25.53 mm/yr for ERA5-Land vs. 6.78 mm/yr for ERA5 and −5.27 mm/yr for observations). However, it is indicated that the trend of the ERA5-Land reanalysis is similar to that of ERA5 at this station when we consider the period after 1952 (about 10.44 mm/yr).

Analysis of all stations with <5 % missing data in the study area (not shown) shows that precipitation trends are downward (94 % for observation, 82 % for ERA5 and 94 % for ERA5-Land). The trends of the

<figure><img src="image_3.png" /><figcaption>Fig. 2. Interannual variability of precipitation at stations in each country. Blue for observations (obs), orange for ERA5 (era) and green for ERA5-Land (lan).</figcaption></figure>
<caption>**Table 1**
Annual precipitation estimates from reanalyses</caption>

Red colors indicate significant trends. Lon stands for longitudinal (°) and Lat stands for latitudinal (°). LAND for ERA5-Land.

<table><thead><tr><th rowspan="2">Country</th><th rowspan="2">Station</th><th rowspan="2">Lon</th><th rowspan="2">Lat</th><th colspan="2">Correlation</th><th colspan="2">RMAE</th><th colspan="3">Trend (mm/yr)</th></tr><tr><th>ERA5</th><th>LAND</th><th>ERA5</th><th>LAND</th><th>ERA5</th><th>OBS</th><th>LAND</th></tr></thead><tbody><tr><td>Ivory Coast</td><td>Bouafle</td><td>-5.75</td><td>6.98</td><td>0.27</td><td>0.33</td><td>22</td><td>21</td><td><strong>6.44</strong></td><td><strong>-9.46</strong></td><td>3.19</td></tr><tr><td>Ghana</td><td>Kumasi</td><td>-1.60</td><td>6.72</td><td>0.5</td><td>0.54</td><td>21</td><td>25</td><td>1.84</td><td><strong>-4.21</strong></td><td>0.68</td></tr><tr><td>Togo</td><td>Sotouboua</td><td>0.98</td><td>8.56</td><td>0.8</td><td>0.79</td><td>19</td><td>24</td><td>0.9</td><td>1.52</td><td>0.44</td></tr><tr><td>Benin</td><td>Parakou</td><td>2.62</td><td>9.36</td><td>0.59</td><td>0.63</td><td>18</td><td>16</td><td>0.82</td><td>-0.95</td><td>0.01</td></tr><tr><td>Nigeria</td><td>Jos</td><td>8.89</td><td>9.85</td><td>0.8</td><td>0.82</td><td>9</td><td>19</td><td>-3.4</td><td>-1.46</td><td>-3.82</td></tr><tr><td>Cameroon</td><td>Yoko</td><td>12.30</td><td>5.53</td><td>0.7</td><td>0.58</td><td>16</td><td>19</td><td>6.78</td><td>-5.27</td><td><strong>25.53</strong></td></tr><tr><td>Guinea</td><td>Siguiri</td><td>-9.14</td><td>11.43</td><td>0.08</td><td>0.18</td><td>15</td><td>15</td><td>-0.5</td><td><strong>-8.3</strong></td><td>-1.42</td></tr><tr><td>Senegal</td><td>Goudiry</td><td>-12.71</td><td>14.18</td><td>0.74</td><td>0.69</td><td>24</td><td>24</td><td>-3.73</td><td><strong>-3.58</strong></td><td>-3.26</td></tr><tr><td>Mauritania</td><td>Kiffa</td><td>-11.39</td><td>16.62</td><td>0.73</td><td>0.65</td><td>38</td><td>36</td><td>-2.21</td><td>-6</td><td>-2.54</td></tr><tr><td>Mali</td><td>Niono</td><td>-5.98</td><td>14.25</td><td>0.9</td><td>0.89</td><td>16</td><td>16</td><td>-3.96</td><td><strong>-7.19</strong></td><td>-3.55</td></tr><tr><td>Burkina Faso</td><td>Saba</td><td>-1.37</td><td>12.35</td><td>0.46</td><td>0.51</td><td>20</td><td>23</td><td>-2.75</td><td>-5.77</td><td>-2.45</td></tr><tr><td>Niger</td><td>Bouza</td><td>6.05</td><td>14.39</td><td>0.53</td><td>0.53</td><td>32</td><td>40</td><td>-3.21</td><td>-0.96</td><td>-2.33</td></tr><tr><td><strong>Mean</strong></td><td></td><td></td><td></td><td><strong>0.59</strong></td><td><strong>0.60</strong></td><td><strong>21</strong></td><td><strong>23</strong></td><td><strong>-0.25</strong></td><td><strong>-4.30</strong></td><td><strong>0.87</strong></td></tr></tbody></table>

ERA5 reanalysis agree with those of the observations for 73.5 % of stations, compared with 79.6 % for ERA5-Land.

### 3.1.2. Performance over the mean annual cycle

Fig. 3 shows the mean annual rainfall cycle for each country in the study area. Analysis of Fig. 3-a shows that the Bouafle station in Ivory Coast has two rainy season peaks. The first peak is observed in June (173 mm) and the second in September (193 mm). Except for the Kumasi station in Ghana (Fig. 3-b), which also appears to have two rainy season peaks, all other stations have a single peak. The latter are generally observed in August (Sotouboua=226 mm, Jos=273 mm, Siguiri=305 mm, Goudiry=216 mm, Kiffa=120 mm, Niono=199 mm, Saba=232 mm, Bouza=175 mm) and only rarely in September (Parakou=221 mm)

or October (Yoko=306 mm). Both reanalyses correctly simulate the rainy season peaks, except for the first peak at the Bouafle station (Fig. 3-a). However, peak shifts were observed at certain stations, such as Kumasi and Parakou (peak in October instead of September in both reanalyses). The correlation performance values confirm that the reanalyses correctly represent the mean annual cycles of rainfall. The mean correlation value for both reanalyses is 0.82. The best correlation for both reanalyses is observed at the JOS station in Nigeria (correlation is 0.90), and the lowest correlations (mean of 0.65) are obtained at the Ghana-KUMASI station (Fig. 3-b). Lower values are also observed at the Bouafle station located in Ivory Coast (Fig. 3-a), probably because the two reanalyses did not correctly represent the first peaks of the rainy season at this station. In terms of bias, we also note that both reanalyses

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Mean annual precipitation cycle in each country
Blue for observations, orange for ERA5, and green for ERA5-Land. Correlation and RMAE are shown in brackets.</figcaption></figure>
show the same performance (mean of RMAE=40 %). The analyses show that both reanalyses generally have low biases (RMAE< 50 %), except the KIFFA station in Mauritania (Fig. 3-i), where the biases are higher for both ERA5 (RMAE=52 %) and ERA5-Land (RMAE=51 %).

### 3.1.3. Performance on daily scale

Fig. 4 shows the distribution of daily precipitation between 1950 and 2016. Analysis of this figure shows that daily precipitation is poorly distributed in the reanalyses. At the Bouafle station in Ivory Coast (Fig. 4-a), the observations show almost no precipitation, while the two reanalyses show values sometimes exceeding 50 mm. In contrast, the reanalyses show zero values, while the observations record values sometimes exceeding 200 mm. The reanalyses overestimate small rainfall events (slope line >0 mm at the origin for all stations) and underestimate heavier rainfall events (slope <1/1). In terms of correlation, ERA5 values ranged from 0.18 (Bouafle and Kumasi stations) to 0.35 (Kiffa station, Fig. 4-i). The correlation ranges of the ERA5-Land reanalysis are almost identical to the latter, including its mean across the twelve stations (c = 0.27). At the same time, the two reanalyses have approximately similar relative biases (RMAE=130 %). For both reanalyses, the RMAE is always greater than 100 %, indicating that the simulated values are sometimes more than twice as high as the actual values.

The poor performance of the two reanalyses on the daily scale is reflected in the extreme precipitation index RX1day (annual precipitation maxima: Table 2). It can be seen that the two reanalyses are sometimes anti-correlated with the observations and show very low correlations (mean of ERA5=0.07 vs. ERA5-Land =-0.01). The performance in terms of bias also appears to be identical for both reanalyses for the RX1day index (RMAE=52 %), as well as for the trend (-0.01 mm/yr for ERA5 vs. -0.03 mm/yr for ERA5-Land). Although these

trends are generally not significant, important differences can be observed. This is the case for the Bouza station in Niger, which shows a significant upward trend of 0.29 mm/yr, while ERA5 shows a downward trend of -0.29 mm/yr, i.e. a difference of 0.54 mm/yr.

### 3.1.4. Performance on hourly precipitation scale

Fig. 5 shows the hourly rainfall distribution at the Cotonou and Sarmanga stations (both in Benin) and the Bangui station in Mali. The reanalyses show a poor distribution of hourly rainfall at all stations. While the observations show a lot of rainfall above 20 mm/hr (CDF about 80 % for hourly rainfall <20 mm), the two reanalyses show average rainfall generally below 15 mm/hr (CDF about 100 %).

For the diurnal cycle, the ERA5 reanalysis correlates well with the observations at the Cotonou station (c = 0.89, Fig. 5-d). The observation data show a single peak at this station (0.27 mm/hr at 09 h). The ERA5 reanalysis also shows a single peak but with a delay of 3 h (0.25 mm/hr at 06 h). The ERA5-Land reanalysis shows two peaks at this station, with a correlation of 0.48. At the Sarmanga station (Fig. 5-e), both reanalyses show a delay of two hours between their peak value (about 0.49 mm/hr at 17 h) and the observed peak (0.23 mm/hr at 19 h). A delay of four hours was noted at the Bangui station located in Mali (Fig. 5-f). The peak was recorded at 21 h with a value of 0.11 mm/hr on the observational data, while the two reanalyses show the peak at 17 h (about 0.13 mm/ hr). At the same station, observations show another peak in the morning (0.12 mm/hr at 17 h), which is not recorded by the two reanalyses.

## 3.2. Revealing reanalyses performance on spatial and temporal scales

### 3.2.1. Performance on spatial variability of annual precipitation

Fig. 6 shows the spatial distribution of annual precipitation over West Africa between 1950 and 2016, and the performance of the

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Distribution of daily rainfall
Orange for ERA5 and green for ERA5-Land. Correlation and RMAE are shown in brackets.</figcaption></figure>
<table><caption>**Table 2**
Reanalysis performance on the RX1day precipitation index.</caption><thead><tr><th rowspan="2">Country</th><th rowspan="2">Station</th><th colspan="2">Correlation</th><th colspan="2">RMAE</th><th colspan="3">Trend (mm/yr)</th></tr><tr><th>ERA5</th><th>LAND</th><th>ERA5</th><th>LAND</th><th>ERA5</th><th>OBS</th><th>LAND</th></tr></thead><tbody><tr><td>Ivory Coast</td><td>Bouafle</td><td>0.03</td><td>0.01</td><td>60</td><td>61</td><td><strong>0.52</strong></td><td>0.10</td><td>0.04</td></tr><tr><td>Ghana</td><td>Kumasi</td><td>-0.01</td><td>-0.18</td><td>58</td><td>63</td><td>0.37</td><td>-0.28</td><td><strong>0.26</strong></td></tr><tr><td>Togo</td><td>Sotouboua</td><td>-0.03</td><td>0.21</td><td>43</td><td>51</td><td>0.17</td><td>0.19</td><td>0.08</td></tr><tr><td>Benin</td><td>Parakou</td><td>0.23</td><td>-0.05</td><td>46</td><td>54</td><td>0.17</td><td>0.29</td><td>0.01</td></tr><tr><td>Nigeria</td><td>Jos</td><td>-0.08</td><td>-0.08</td><td>48</td><td>47</td><td>0.01</td><td>0.09</td><td>-0.10</td></tr><tr><td>Cameroon</td><td>Yoko</td><td>-0.08</td><td>0.11</td><td>53</td><td>56</td><td>-0.33</td><td>-0.07</td><td>0.24</td></tr><tr><td>Guinea</td><td>Siguiri</td><td>0.19</td><td>-0.09</td><td>37</td><td>43</td><td>-0.07</td><td>-0.18</td><td>-0.32</td></tr><tr><td>Senegal</td><td>Goudiry</td><td>0.02</td><td>-0.17</td><td>54</td><td>50</td><td>-0.26</td><td><strong>-0.53</strong></td><td>0.08</td></tr><tr><td>Mauritania</td><td>Kiffa</td><td>0.51</td><td>0.03</td><td>50</td><td>56</td><td>-0.19</td><td>-0.66</td><td>-0.11</td></tr><tr><td>Mali</td><td>Niono</td><td>-0.05</td><td>0.05</td><td>53</td><td>42</td><td>-0.35</td><td>-0.39</td><td><strong>-0.53</strong></td></tr><tr><td>Burkina Faso</td><td>Saba</td><td>0.08</td><td>-0.11</td><td>46</td><td>50</td><td>0.17</td><td>-0.02</td><td>0.15</td></tr><tr><td>Niger</td><td>Bouza</td><td>0.08</td><td>0.15</td><td>60</td><td>56</td><td>-0.29</td><td><strong>0.29</strong></td><td>-0.17</td></tr><tr><td><strong>Mean</strong></td><td></td><td><strong>0.07</strong></td><td><strong>-0.01</strong></td><td><strong>51</strong></td><td><strong>52</strong></td><td><strong>-0.01</strong></td><td><strong>-0.10</strong></td><td><strong>-0.03</strong></td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Hourly rainfall distribution performance</figcaption></figure>

The first line shows the CDF of rainfall above 2 mm/hr with precision on the mean annual precipitation (mm), and the second line shows the diurnal cycle. Blue for observations (obs), orange for ERA5 (era), and green for ERA5-Land (lan). c=correlation.

reanalyses at all stations. It can be seen that the ERA5 reanalysis (Fig. 6-a) shows higher annual precipitation in the south, which continues to decrease with increasing altitude. This is illustrated by the fact that the annual precipitation over countries resting on the Atlantic Ocean is higher than the annual precipitation in Mali, Burkina-Faso and Niger (all above 10° latitude). In the former, the annual precipitation generally exceeds 1000 mm, while in the latter, the mean annual precipitation is sometimes <600 mm. In Guinea, annual precipitation exceeds 1000 mm in all pixels. In Togo, a mean annual precipitation of 1000 mm is observed up to 11° latitude, while in Cameroon, it is observed below 9° latitude. Since Guinea, Togo, and Cameroon are located on negative (longitude = -15°), central (longitude = 0°), and positive (longitude = 15°) longitudes, we can conclude that there is a slight East-West gradient in annual rainfall.

The spatial variability of mean annual precipitation in the ERA5-Land (Fig. 6-b) is almost identical to that of ERA5. It also shows that the southern side receives more rainfall than the north. Above 15° latitude, mean annual precipitation is generally <600 mm. However, there are slight differences between the two reanalyses. In south-west Mali, the ERA5 reanalysis shows annual precipitation around 1150 mm, compared to 950 mm for ERA5-Land, a difference of up to 200 mm.

The same is true for Burkina, which shows higher mean precipitation in ERA5 (650 mm) compared to LAND (610 mm).

Observational data (Fig. 6-c) also showed the less rainy north (under 600 mm) compared to the rainier south (over 1000 mm), showing that reanalyses agree well with the latter. The largest differences between observational and reanalysis data were found in Nigeria, especially in the central part of the country, which is probably due to the lack of in situ measurement stations in the country. On the other hand, both reanalyses show less precipitation in the north of the study area, compared to the observations.

The analysis performance at the point station shows that both reanalyses have significant biases (RMAE>30%) at all stations in Niger, whether using the SNP (2nd row) or IDW (3rd row) method, which justifies the difficulties of the reanalyses in capturing precipitation in the Sahel region. In this country, we also note that there are more negative differences between the RMAE of ERA5 and the RMAE of ERA5-Land, especially with the IDW method, which justifies the fact that ERA5 has the lowest biases in Niger. The situation is similar in Cameroon, but is reversed in other countries such as Nigeria, where ERA5-Land has the lowest biases.

The performance of the reanalyses is also evaluated on a spatial
<figure><img src="image_7.png" /><figcaption>Fig. 6. Spatial variability of a) precipitation on ERA5, b) ERA5-Land, c) observations, and RMAE performance at all stations, using SNP (2^nd^ row) or IDW (3^rd^ row). Mean values are shown in red.</figcaption></figure>

scale, using annual precipitation time series from all stations (not pre-
sented). It shows that for the year 2000, the correlation on ERA5 using
the SNP method is 0.85. Using the IDW method, we observe almost
similar results (0.86). This performance is consistently better than that
of ERA5-Land, whether using SNP or IDW (0.81). The same is true for all
years of the study. Concerning the bias in the reanalyses, ERA5 also
performed slightly better (RMAE<350) than ERA5-Land (RMAE>350).
The IDW method seems to perform better than the SNP method in both
reanalyses.

3.2.2. Performance at annual, monthly, and daily time scales

Fig. 7 shows the daily, monthly, and annual precipitation correla-
tions of the reanalyses (on the left) for all stations in the study area.
Analysis of the figure shows that the correlation for daily precipitation is
always positive in all countries and at all stations. In Ivory Coast, daily
correlations (Fig. 7-a) ranged from 0.11 to 0.31 for ERA5 and from 0.14
to 0.33 for ERA5-Land, using the SNP method. The mean values for the
two reanalyses are almost identical when using the SNP method (cor-
relation is 0.2). When using the IDW method, the mean values for both
reanalyses are also almost identical (0.22). The comparison of means
between the selection methods shows no significant difference.

<figure><img src="image_8.png" /><figcaption>Fig. 7. Correlation performance (left) of ERA5 (orange) and ERA5-Land (green) reanalysis in each country, using SNP (solid line) or IDW (dotted line).</figcaption></figure>
However, the IDW correlation is slightly better than SNP for stations located in the same country. The minima and maxima for both methods are also generally in the same range. On a monthly scale (Fig. 7-b), the best correlations can be seen in the study area. It varies between 0.41 (mean for stations in Cameroon) and 0.71 (in Guinea). Except for Ivory Coast, where some stations have negative values, the correlations are generally above 0.25 for all stations. As on the daily scale, the highest monthly correlations are obtained in Guinea, with values above 0.5 at all stations in this country. The performance is also better on an annual scale (Fig. 7-c), with correlation values sometimes close to 1. However, poor performance is observed at some stations, notably in Ivory Coast and Ghana (negative correlation in both reanalyses). Except for Guinea, there are significant differences between the minima and maxima of stations located within the same country.

Fig. 7 shows the RMAE values (right) for daily, monthly, and annual precipitation from the ERA5 and ERA5-Land reanalyses, for all stations in the study area. The analysis of this figure shows that on the annual scale (Fig. 7-d), the two reanalyses produce identical mean values within Ivory Coast using the SNP method (102 % for ERA5 vs. 111 % for ERA5-Land). However, the differences in the maxima are more pronounced (367 % for ERA5 vs. 399 % for ERA5-Land). Using the IDW method, the mean RMAE value of the ERA5 reanalysis (100 %) is almost identical to that of the ERA5-Land reanalysis (RMAE=108 %). The same is true for all countries and all time scales.

Overall, the two reanalyses performed well regarding mean correlation on the annual (0.61) and monthly (0.79) scales, with mean RMAEs of <100 % for both methods. The mean correlation of the two reanalyses on the daily scale is 0.25, with an RMAE of 137 %. For the extreme precipitation index RX1days (not shown), the correlation of ERA5 and LAND over the entire study area averaged 0.07 and 0.06. The

biases are 53 % and 55 % for ERA5 and ERA5-Land, respectively.

### 3.2.3. Performance on the hourly scale

Fig. 8 shows the performance of ERA5 and ERA5-Land on RMAE and Correlation, when using the SNP or IDW method. The analysis of the two methods showed some differences, especially at the Cotonou station. Using the SNP method, the ERA5-Land reanalysis showed 96.72 % for the percentage of rainfall between [0, 1) mm/hr, compared to 98.29 % using the IDW method. The RMAE values also showed a significant difference depending on the method used only for the Cotonou station, especially for the ERA5-Land reanalysis rainfall between [0, 1) mm/hr (1092 % with the SNP method vs. 613 % with the IDW method). The situation seems to be similar for other classes, e.g., for precipitation between [1, 2) mm/hr. For the latter, the RMAE for the SNP method (Fig. 8-b) is 263 %, while the RMAE for the IDW method is 96 %, which is almost a threefold increase. There are also slight differences in the correlation between the two methods, both for ERA5 (Fig. 8-c) and ERA5-Land (Fig. 8-d). This is the case for rainfall of [2, 3) mm/hr at the Bangui station, where the ERA5 reanalysis shows a correlation of 0.25 when using the SNP method compared to 0.45 when using IDW. This is also the case for rainfall of [3, 4) mm/hr, where the ERA5 reanalysis shows a correlation of 0.79 when using the SNP method compared to 0.48 when using IDW.

Hourly time series correlations show a value of 0.06 for both reanalyses, using either the SNP or IDW method. RMAE values averaged 170 % for both reanalyses and both methods.

## 4. Summary and Discussion

This study evaluated and compared two variants of the ECMWF

<figure><img src="image_9.png" /><figcaption>**Fig. 8.** Performance on RMAE and Correlation, when using the SNP or IDW method. Blue indicates the best performance, and yellow indicates poor performance.</figcaption></figure>
reanalyses, namely ERA5 and ERA5-Land. The analyses showed a decreasing trend of the observed precipitation over the study area (94 % of the stations). The observational data show a unimodal peak in the Sahel, often in August. Both reanalyses performed well in terms of monthly and annual variability (correlation >0.5 and RMAE<50 %). These results are consistent with several studies that have shown that the ECMWF 5th generation reanalyses capture interannual and monthly rainfall variability well (Qin et al., 2021; Bodjrenou et al., 2023a, 2024; Dai, 2024). Bodjrenou et al. (2023a) report good performance for ERA5-Land reanalysis precipitation in the three climate zones of Benin on monthly and annual scales. Gbode et al. (2023) report that the annual cycle of rainfall is better captured by ERA5 in the three climate zones of the region, especially in the Savanna and the coastal region of Guinea.

The results show that both reanalyses perform poorly on daily and hourly time scales. At some stations, they are sometimes characterized by anti-correlations or RMAE values higher than 100 %. The anti-correlations are always shown in RX1day (Table 1), which can justify that two reanalyses poorly simulate the extreme precipitation over West Africa (Gbode et al., 2023). Studies by Ji et al. (2024) based on R10mm, R99p, CWD, and Rx1day came to the same conclusion. On the diurnal precipitation cycle, it is shown that there are delays of about three hours in the reanalyses, as shown in the studies by Dai (2024) and Bodjrenou et al. (2024). Our results show that in Mali, the peak delay can be up to four hours, which is probably due to the difference in time zones. The large differences, especially in terms of the RMAE for precipitation of [0, 1) mm/hr, justify that the reanalysis is less efficient on the hourly scale. This can be explained by the fact that precipitation in ERA5 reanalyses does not include fog, rain or dew which evaporates in the atmosphere before reaching the Earth's surface (Hersbach et al., 2023).

In terms of spatial variability, the results show that the mean pre-
cipitation at each altitude tends to decrease with increasing longitude.
This can be explained by the influence of the Atlantic Ocean on which
the study area rests (Gimeno et al., 2012). It should also be noted that
the two reanalyses showed good correlation at certain point stations, but
this can be lower at other stations, which is certainly due to the influence
of the topography (Cucchi et al., 2020; Bodjrenou et al., 2023a). The
largest differences between observational and reanalysis data are found
in Nigeria, especially in the central part of the country, probably due to
the lack of in situ measurement stations. The biases would have been
reduced if missing data had been filled in before spatialization, using
sophisticated methods such as inverse distance weighting or Markov
chain models (Camuffo et al., 2022; Yagaso et al., 2024). Both reanalyses
show a more pronounced south-north gradient compared to the obser-
vations. This may also be due to the influence of topography, demon-
strating the difficulties of reanalyses in simulating climate in regions
with very different topography (Bodjrenou et al., 2023a). In the same
sense, Yagaso et al. (2024) showed that the complex topography of
Ethiopia has a significant influence on the simulation of its climate by
the ERA5 reanalysis. The overall average shows that SNP is almost
identical to IDW, which is probably because the significant biases shown
in mountainous areas are compensated for in non-mountainous areas, or
even valley areas, and vice-versa. The biases of ERA5-Land are some-
times higher than those of ERA5, showing that the latter offers the best
performance. However, the opposite effects are sometimes observed
when evaluating reanalyses based on correlation (better correlation vs.
poor RMAE on ERA5). Similar results have been found in the studies of
Ahmed et al. (2024), allowing them to claim that no single reanalysis is
better than the other in all aspects. Furthermore, the RMAE values show
that the performance of the reanalyses ranges from better to worse, from
annual (22 %), monthly (40 %), daily (128 %), to hourly (170 %).
Correlations showed better performance at the monthly scale than at the
annual scale, thus confirming the need to aggregate multiple evaluators
to effectively assess reanalyses (Bodjrenou et al., 2023a).

6. Conclusion and recommendation

The ECMWF fifth-generation reanalyses are valid alternatives for the continuity of hydrological studies in West Africa. They capture the annual and monthly rainfall cycle well, but their daily and hourly distributions are challenging. The bias adjustment techniques tested in some studies can help to overcome the poor performance of small-scale reanalyses. We do not recommend favoring one reanalysis over the other as their performance is almost similar. Furthermore, we do not favor one selection method over the other, but authors who choose one of the methods should be aware that they accept a certain degree of uncertainty in their study, which depends on the influence of the topography of the environment. Overall, we believe that this study will inform C3S monitoring activities to improve precipitation data from the fifth generation reanalyses. It will undoubtedly also stimulate the scientific community to develop effective techniques to correct reanalysis data and, in particular, to improve their performance in describing the daily and hourly cycle of the product.

Author declaration

The contact author has declared that none of the authors has any
competing interests

Data availability

- Reanalysis data are freely available on the Copernicus website: https://cds.climate.copernicus.eu/cdsapp#!/search?type=dataset

- Daily data are available from the agencies responsible for their collection and processing in each country.

- AMMA-CATCH data are freely available at: https://amma-catch.osug.fr/

CRediT authorship contribution statement

**René Bodjrenou:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Luc Ollivier Sintondji:** Writing – original draft, Visualization, Validation, Supervision, Funding acquisition, Formal analysis, Conceptualization. **Françoise Comandan:** Writing – original draft, Visualization, Validation, Supervision, Methodology, Investigation, Funding acquisition, Data curation.

Declaration of competing interest

None.

Acknowledgments

We thank the “Institut de Recherche pour le Développement” and the
“Dutch Embassy’s OmiDelta project” for funding the research.

Supplementary materials

Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.envadv.2025.100636.

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