We use hourly data to examine Congo Basin MCSs for the period of 21–25 November 2023. The National Centers for Environmental Prediction (NCEP)/Climate Prediction Center (CPC) L3 Half Hourly 4km Global Merged infrared (IR) Tb data (Janowiak et al., 2017) is used to detect MCSs. This Tb data has been widely used in tracking MCSs, especially over tropical regions (e.g., Feng et al., 2021, 2023a, 2025; Chen et al., 2023; Prein et al., 2024; Muetzelfeldt et al., 2025). Due to the sparsity of gauge networks over the Congo Basin in recent decades (Nicholson et al., 2018), we will mainly use satellite precipitation data. Since MCS detection requires high-resolution, sub-daily data, we analyze precipitation associated with identified MCSs using half-hourly precipitation data from the CPC Morphing Technique (CMORPH) Climate Data Record (CDR) with an 8 km horizontal resolution (Xie et al., 2019) and the Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals (IMERG) Final Precipitation with a 0.1° horizontal resolution (Huffman et al., 2023).

In addition to hourly data, daily precipitation data are used to analyze daily and monthly precipitation over the Congo Basin. These datasets include CMORPH CDR (0.25° horizontal resolution, 1998–2024) (Xie et al., 2019), GPM IMERG Final Precipitation (0.1° horizontal resolution, 2001–2024) (Huffman et al., 2023), and the CPC Global Unified Gauge-Based Analysis of Daily Precipitation (0.5° horizontal resolution, 1979–2024) (Chen et al., 2008). Additionally, daily runoff data are obtained from the Global Land Data Assimilation System (GLDAS) Catchment Land Surface Model L4 (0.25° horizontal resolution, 2004–2024) (Li et al., 2020).

MPAS-A is a global cloud-resolving model representing a new category of atmospheric models and is a participant in DYAMOND, the first intercomparison project of such models (Stevens et al., 2019). It solves non-hydrostatic equations using kilometer-scale global meshes and simulates deep convection explicitly (Skamarock et al., 2012; Satoh et al., 2019). In this study, we use MPAS-A version 8 modified to output variables at 27 isobaric levels (Núñez Ocasio et al., 2024). We use a variable-resolution, 60–3 km global mesh, and the Congo Basin is roughly within the 3 km domain (Fig. 1a). The model uses 55 vertical levels up to ∼ 30 km height.

The model physics follows the standard mesoscale-reference suite incorporating the new scale-aware Tiedtke convection scheme, which is newly added to MPAS-A version 8 and suitable for a convection-permitting mesh with grid spacing below 10 km (Wang, 2022). The scale-aware Tiedtke scheme effectively reduces deep convection by decreasing the convective portion of total surface precipitation, and also ensures smooth handling of convection across mesh transition zones when applied to a variable mesh in MPAS-A (Wang, 2022). The other schemes of the physics suite include the WSM6 microphysics scheme (Hong and Lim, 2006), the Noah land surface scheme (Niu et al., 2011), the YSU boundary layer scheme (Hong et al., 2006), the Monin-Obukhov surface layer scheme (Jiménez et al., 2012), the RRTMG shortwave and longwave radiation scheme (Iacono et al., 2008), and the Xu-Randall subgrid cloud fraction scheme (Xu and Randall, 1996).

We use the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5; Hersbach et al., 2020), including a complete set of atmospheric pressure level and surface variables, to initialize model simulations. The experiments begin at 12:00 Coordinated Universal Time (UTC) on 21 November 2023, running for four days, with the first six hours discarded for spin-up. The model produces hourly outputs at 27 isobaric levels. In addition to the primary simulation discussed above, we have conducted additional simulations to ensure that our conclusions were not influenced by model stochasticity. In the sensitivity tests, random perturbations were added to the 1000 hPa potential temperature field following a Gaussian distribution with a standard deviation of 0.6 K, as in Núñez Ocasio et al. (2024). Since the results of the sensitivity tests closely resemble those of the unperturbed simulation, we present only the model simulation without random perturbations in this study.

The Congo Basin MCS tracking is conducted objectively using the latest version of TAMS, an open-source, Python-based package for tracking and classifying MCSs (Núñez Ocasio and Moon, 2024). Following the MCS tracking algorithm described in Núñez Ocasio et al. (2020b) and Núñez Ocasio and Moon (2024), hourly Tb contours are used to identify cloud elements (CEs), selecting < 235 K regions that contain embedded < 219 K areas of at least 4000 km². In MPAS-A, Tb is derived from the model's outgoing longwave radiation output following the method of Yang and Slingo (2001). MCS tracks are determined by linking CEs from the current time step to those from the previous step based on maximum CE polygon overlap. Once an MCS is identified and tracked, its complete trajectory is analyzed, allowing its entire lifespan to be assigned to a single classification. Table 1 shows the MCS classification criteria adopted from Núñez Ocasio and Moon (2024) for use in TAMS, which includes four categories: mesoscale convective complex (MCC), convective cloud cluster (CCC), disorganized long-lived (DLL), and disorganized short-lived (DSL). Other studies (e.g., Maddox, 1980; Evans and Shemo, 1996; Tsakraklides and Evans, 2003; Núñez Ocasio et al., 2020b) have similar criteria to classify MCS. TAMS outputs include the latitude and longitude centroids of < 219 and < 235 K regions for each MCS at every time step, along with additional statistics such as area, duration, and mean precipitation for each identified MCS.

The Congo Basin spans a vast area across Central Africa, with elevation varying significantly. It primarily consists of lowlands in the west, central, and northern regions, surrounded by higher elevations in the south and east (Fig. 1b). It is home to a complex river system, dominated by the Congo River, the second largest in the world by discharge. The seasonal cycle of Congo Basin domain-averaged precipitation exhibits a biannual pattern, with the first peak occurring in March–April and the second in October–November (Fig. 1c). The satellite data generally show a higher daily precipitation rate compared to gauge-based data, a discrepancy that has been widely studied in comparisons with gauging station networks (e.g., Hughes, 2006). This biannual cycle is recognized as a key characteristic of Congo Basin rainfall (e.g., Washington et al., 2013; Pokam et al., 2014; Dyer et al., 2017), though it is more pronounced south of the equator (Nicholson, 2022).

Between November 2023 and January 2024, the Democratic Republic of the Congo and Congo-Brazzaville experienced their worst flooding in the past 60 years (Davies, 2024; Joachim et al., 2024). Figure 2a shows daily runoff in November 2023 (bars) compared with the climatological daily mean (gray line) and the climatological mean ±1 standard deviation (dashed blue lines). Extreme runoff anomalies, reaching up to 50 % above the climatological mean, occurred during 21–25 November, with values exceeding one standard deviation between 23–25 November. All three rainfall datasets also exhibit consecutive days of positive precipitation anomalies, especially in GPM IMERG and CPC gauge-based data (Fig. 2b–d). GPM IMERG shows three consecutive days above +1 standard deviation. In addition to the precipitation anomalies observed during 21–25 November, the preceding dry conditions – particularly the near- or below- -1 standard deviation of precipitation prior to 15 November, as indicated by CMORPH CDR – may also contribute to the extreme flooding. The antecedent dry conditions would have reduced soil moisture retention capacity, leading to increased runoff during subsequent heavy rainfall events. Such conditions could enhance flood risks, even when the precipitation event itself is not exceptionally intense (e.g., Barendrecht et al., 2024). Since MCSs account for approximately 80 % of the total rainfall in the Congo Basin (e.g., Mohr et al., 1999; Nicholson, 2022; Andrews et al., 2024), this study focuses on the period 21–25 November 2023, to improve the process-level understanding of MCSs in this region.

Following the method in Sect. 2, we apply TAMS to objectively identify, track, and classify MCSs. Figure 3a displays all identified MCSs that passed through the Congo Basin during the study period in observations. For simplicity, only the initial and final locations are shown. Consistent with previous studies (Jackson et al., 2009; Laing et al., 2011; Hartman, 2020), most MCSs propagate westward, with a substantial number originating in the lee side of the high terrain of the Great Rift Valley. Following the strictest criteria in Table 1, MCCs with an elliptical shape (ε=1-b2/a2≤0.7) generally initiate in the eastern portion of the basin and propagate the longest distance; one MCC case even moves from the eastern boundary to the western boundary. CCCs have the second-longest moving distances, slightly longer than DLLs. By definition, DSLs are localized, with the shortest moving distances, and they can occur over most areas of the basin.

In this section, we evaluate how well MPAS-A simulates MCSs by conducting simulations for 21–25 November 2023, as described in Sect. 2. We do not expect the model to capture every individual MCS track in MPAS-A; instead, our goal is to assess whether long-duration or long-track MCS events and the overall MCS statistics are realistically represented. The model successfully simulates the general westward propagation of MCSs, but the number of MCC cases is lower than in observations (Fig. 3b). Notably, the long-track MCC case over the southern basin (referred to as MCC-south) is well captured in terms of spatial location. Figure 3c–d compares MCS characteristics between observations and model simulations. The MCS duration is well simulated, particularly for DSL, DLL, and CCC (Fig. 3c). MCCs typically last over 15 h, with one observed case lasting 27 h. The two simulated MCCs last 15 and 20 h, respectively, consistent with observations. Compared to observations, the model exhibits higher rainfall magnitudes for all four categories (Fig. 3d), consistent with previous studies (e.g., Raghavendra et al., 2022; Feng et al., 2023b) comparing rainfall between observations and WRF, which form the basis of MPAS-A's physics schemes (Skamarock et al., 2012). These differences may arise from uncertainties in both the model simulations and the observational data.

We compare the spatial distribution of the observed MCC-south case with that from MPAS-A simulations. The observed Tb shows that MCC-south originates from the Great Rift Valley (Fig. 4a), propagates westward during the late afternoon and evening (local time UTC +01:00 or +02:00, Fig. 4b–c), and weakens in the following morning (Fig. 4d). The mean area of the observed MCC-south (averaged across all time steps) is 6.7 × 10⁵ km², covering nearly 30 % of the Congo Basin, and the mean area enclosed by the 219 K contours is 3.2 × 10⁵ km², approximately half of the total MCC area. The simulated MCC-south originates from the Great Rift Valley, though slightly farther south, and propagates westward across the Congo Basin (Fig. 4e–h). While the simulated Tb is more scattered and the mean MCC area is 12 % smaller than observed, the model still successfully captures the timing and general location of MCC-south.

To better understand the evolution of Tb and associated rainfall, we present the Hovmöller diagram along the MCC track. Figure 5a illustrates the observed Tb evolution for MCC-south, which originates near 29° E, featuring three centers (Tb < 210 K) along its path, excluding the one east of 30° E, as it does not belong to MCC-south. The simulated Tb evolution shares a similar pattern with observations, but during the initial stage, Tb magnitudes are smaller, and the Tb pattern appears more scattered (Fig. 5b). The simulated MCC strengthens (Tb decreases) significantly during the mid-stage of MCC-south (around 16:00:00Z), warranting further analyses to investigate the underlying mechanisms. While rainfall from the two observations generally aligns with Tb from NCEP/CPC data, CMORPH shows lower rainfall in the early–mid stages and higher rainfall in the later stage (Fig. 5c). Consistent with Fig. 3d, the simulated rainfall magnitudes exceed those in observations (Fig. 5d). The high rainfall magnitudes in cloud-resolving models have also been observed in previous studies (e.g., Raghavendra et al., 2022; Feng et al., 2023b). Despite these differences, the results above suggest that MPAS-A shows promise in simulating long-lived, long-track MCC over the Congo Basin.

Next, we utilize the model to further understand mesoscale convective processes by comparing two representative MCC cases, MCC-south and another MCC case over the northern basin, referred to as MCC-north (Fig. 3b). MCC-north originates during the evening over the northeastern boundary of the Congo Basin, where the elevation ranges from approximately 700 to 1000 m (Fig. 6a). The system then propagates westward through midnight (Fig. 6b) and weakens over lowlands, where elevations drop to 400–700 m or lower, by early morning (Fig. 6c). The mean area of the simulated MCC-north is about 22 % of that of MCC-south, with a shorter lifecycle of 15 h compared to 20 h for MCC-south. Additionally, its moving distance is approximately 35 % of that of MCC-south. Overall, MCC-north is smaller in size, shorter in duration, and less extensive in movement compared to MCC-south. Figure 6d–e presents the Hovmöller diagrams of Tb and rainfall along the MCC-north track. MCC-north intensifies rapidly after its initiation, characterized by low Tb centers and rainfall exceeding 10 mm h⁻¹ during its early stage (before 19:00:00Z). Contrary to MCC-south, the simulated MCC-north weakens (Tb increases) significantly from the mid-stage (around 21:00:00Z) (Fig. 6d). We investigate the mechanisms responsible for such difference in Sect. 3.3.

Figure 7a–b shows convective available potential energy (CAPE), which reflects atmospheric instability and the potential for convection, and convective inhibition (CIN), which represents the energy barrier that must be overcome for convection to initiate, for the two MCC cases. For MCC-south, large CAPE values, as high as 2000 J kg⁻¹, consistently preceding the track and accompanied by CIN values below 50 J kg⁻¹, create favorable conditions for convection development. For MCC-north, CAPE is significantly lower, with CAPE values dropping below 1200 J kg⁻¹, and CIN also increases, during the mid-stage (around 21:00:00Z). In addition, we examine the vertical profile of atmospheric energy. Following Hobbs and Wallace (2006), moist static energy (MSE), which represents total energy of an air parcel, is expressed as: 1MSE=Lvq+cpT+Φ, where Lv is latent heat of vaporization, q is specific humidity, cp is specific heat capacity at constant pressure, T is air temperature, and Φ is geopotential. Figure 7c shows that MSE decreases with height for both MCC cases, indicating a convectively unstable environment favorable for MCC developments. MCC-south exhibits higher MSE from 850 hPa to the mid-troposphere, suggesting greater potential for convection and stronger moist updrafts.

In this section, we examine dynamic factors driving the different evolutions of the two MCC cases. Although previous studies have explored the relationship between MCSs over the Congo Basin or equatorial Africa and dynamic factors such as vertical wind shear and AEJ (Nguyen and Duvel, 2008; Jackson et al., 2009; Laing et al., 2011), they primarily relied on low-resolution reanalysis data from a climate perspective without quantitative analyses based on MCS tracks. Weather-focused investigations, such as quantifying the influence of these dynamic factors on MCS developments along its track, require cloud-resolving model simulations (Laing et al., 2011).

During the mid-stage (16:00:00Z) of MCC-south, its convection, linked to low-tropospheric convergence (blue shading) and updrafts (red contours), is concentrated over elevated terrain (Fig. 8a), with a cold pool region behind it (Fig. 9a). Westerly mountain-valley breezes ahead of the MCC lift moisture-rich air from the lower troposphere, providing abundant latent energy to fuel the system. As the MCC propagates into the valley, convergence intensifies from the surface to the mid-troposphere, accompanied by enhanced vertical wind shear between the lower and mid-troposphere ahead of the center (Fig. 8b) and a narrow cold pool region trailing the MCC center (Fig. 9b). The vertical wind shear ahead of the MCC-south track is generally easterly. For MCC-north, however, the system reaches the lowland forest region during its mid-stage (21:00:00Z), where the lower troposphere is dominated by divergence (orange shading), inhibiting the upward transport of low-level moisture to the mid-troposphere (Fig. 8c). The cold pool behind the MCC center extends broadly in the zonal direction (from 26 to 29° E, ∼ 300 km), with temperatures dropping by up to 4 K (291 K vs. 295 K) (Fig. 9c).

Compared to MCC-south, lower-level westerly winds are absent ahead of the MCC-north, resulting in generally weaker easterly wind shear (Fig. 8c–d); the cold pool is generally stronger and extends broader zonally in MCC-north (Fig. 9c–d). According to Rotunno et al. (1988), that is RKW theory, the weaker vertical wind shear may result from an overly strong cold pool, characterized by greater intensity and broader horizontal extent in this case, causing the convective system to tilt upshear and limiting MCC developments (e.g., Schumacher and Rasmussen, 2020; Kirshbaum et al., 2025). In contrast, MCC-south features a more favorable balance between vertical wind shear and cold pool strength, approaching the “optimal state” for MCC developments during the mid-stage.

We further quantify how vertical wind shear influences MCC evolution by using a simple linear regression model (e.g., Zhao and Zhang, 2022; Zhao and Fu, 2022): 2Tb=α+β×∇Ux, where Tb is brightness temperature at the MCC center, ∇Ux is vertical zonal wind shear (U600-U875 for MCC-south and U600-U900 for MCC-north) with the longitudinal distance (x; i.e., degree) relative to the MCC center, and α and β are regression coefficients. We calculate coefficient of determination (R2=1-RSS/TSS, where RSS is sum of squares of residuals and TSS is total sum of squares) to assess how much of the variance in Tb can be linked to wind shear.

Figure 10a shows that vertical wind shear located approximately 1.5 to 4° ahead of the MCC-south center corresponds to 35 %–65 % of the total variance in Tb, with the highest values around 2° W of the MCC center. This result suggests that vertical wind shear ahead of the MCC-south center plays a key role in modulating convection, corresponding up to 65 % of the Tb variance, and that this favorable environment may promote the formation of a gust front ahead of the MCC center (Schumacher and Rasmussen, 2020). Strong convection (Tb < 220 K) is associated with easterly wind shear exceeding 10 m s⁻¹ (Fig. 10b). In contrast, for MCC-north, the wind shear that significantly links to Tb variance (∼ 60 %) is confined near the center of the MCC, with a sharp decline beyond 1° W of the center (Fig. 10c), indicating that the generation of convective cells is primarily close to the cold pool. During several timesteps along the track, MCC-north exhibits weak or even westerly shear (positive values), despite persistent easterly flow in the mid-troposphere along the MCC track (Fig. 10d). This suggests that the lower troposphere experiences relatively stronger easterly winds than the mid-level.

Therefore, compared to MCC-north, the longer duration and longer track of MCC-south are likely associated with a more favorable pre-existing wind shear structure extending up to ∼ 400 km ahead of the system. According to the RKW theory, which relates the balance between cold-pool outflow and vertical wind shear to convective organization (e.g., Rotunno et al., 1988; Schumacher and Rasmussen, 2020; Kirshbaum et al., 2025), this shear environment can achieve an “optimal” balance with the cold pool, effectively interacting with its outflow to lift warm, moist air, sustain updrafts, and promote the continued development and forward propagation of the MCC. These differences between the two MCC cases highlight the role of latitude and topography in modulating the impacts of environmental factors on MCS developments.

Finally, we investigate the linkage between the AEJ and the vertical wind shear associated with the two MCC cases. The AEJ system, consisting of both a northern and a southern branch (AEJ-N and AEJ-S), can promote mid-tropospheric moisture convergence within the right entrance region of the jet, thereby enhancing rainfall (Uccellini and Johnson, 1979; Jackson et al., 2009). Figure 11a shows zonal winds at 650 hPa averaged over the study period. The AEJ centers are typically identified by zonal wind speeds exceeding 6 m s⁻¹, and their approximate locations in this figure are consistent with previous studies (e.g., Jackson et al., 2009). MCC-north and MCC-south are located near the AEJ-N and AEJ-S, respectively. We define AEJ-N and AEJ-S indices as the domain-averaged zonal winds within the yellow boxes and calculate their correlations with vertical wind shear along MCC tracks. The strength of AEJ-N and AEJ-S is strongly positively correlated (correlation > 0.7) with the vertical shear associated with MCC-north and MCC-south, respectively, based on the temporal evolution of the MCCs throughout their lifecycles (not shown). This suggests that the AEJ helps establish the wind shear conditions necessary for MCC developments. This is consistent with previous studies emphasizing the AEJ's role in shear propagation over the West African monsoon region (Núñez Ocasio et al., 2020a). However, AEJ-N is notably stronger than AEJ-S (Fig. 11b vs. 11c) and is associated with weaker low-level westerlies (lighter pink shading), which may lead to overall weaker convection in MCC-north.