Contact Authors
Flavio Pons, Science Partners, IPSL, France 📨 flavio.pons@science-partners.fr 🗣️English, French, Italian
Tommaso Alberti, INGV, Italy 📨 tommaso.alberti@ingv.it 🗣️Italian, English
Valerio Lucarini, Leicester University 📨v.lucarini@leicester.ac.uk 🗣️Italian, English
Davide Faranda, IPSL-CNRS, France 📨davide.faranda@lsce.ipsl.fr 🗣️English,French, Italian
Citation
Pons, F. M. E., Alberti, T., Lucarini, V., & Faranda, D. (2025). Heavy rain in May 2025 Democratic Republic of the Congo floods locally intensified by human-driven climate change. ClimaMeter, Institut Pierre Simon Laplace, CNRS. https://doi.org/10.5281/zenodo.15394334
Press Summary
Meteorological conditions similar to that causing floods in Democratic Republic of the Congo are up to 7 mm/day (up to 25%) wetter over the South Kivu province in the present than they have been in the past. Temperatures are up to 1.5 ºC warmer in the present compared to the past.
This event was associated with very exceptional meteorological conditions.
We mostly ascribe the increase in precipitation of the Democratic Republic of the Congo floods to human driven climate change and natural climate variability likely played a modest role.
Event Description
Between 8 and 9 May 2025 heavy rainfall affected the South Kivu province in the Democratic Republic of the Congo (DRC), causing the Kasaba River to overflow, leading to severe flooding in the Kasaba village, on the Lake Tanganyika shore. The isolation of the area and the lack of connection to the internet made communication and rescue operations difficult, although several news outlets reported around 100 confirmed fatalities from the event. The meteorological conditions were characterized by weak positive surface pressure anomalies over the area, and weak negative anomalies to the northwest. Temperatures were mostly lower than normal over the region, with negative anomalies up to -2°C in southern DRC. Precipitation was widespread in the region during the event, with peak accumulation to the north and west of the most affected part of the Lake Tanganyika shore. Moderate winds were observed over the region. The data used in this analysis come from the ERA5 reanalysis, which combines model output with available observational data, including ground stations and satellite measurements. Differences with localized station observations may occur.
Climate and Data Background for the Analysis
The IPCC AR6 WG1 highlights the influence of global warming on both river floods and intense precipitation occurrence in several parts of Africa, including the region affected by these floods. Despite large fluctuations in the frequency of river floods in the years between 1990 and 2014, the IPCC attributes medium confidence to a general upward trend in the occurrence of flood events in Africa.
Under future global warming scenarios, episodes of extreme river discharge are projected to increase at least 10% compared to the 1971-2000 period by the end of the century for RCP8.5 for most tropical African rivers. The magnitude of river flooding events is also projected to increase in humid tropical regions by 2050. While there is a certain degree of spatial variation and uncertainty, on average over the entire African continent, the return time of flood events currently characterized by 100-year return periods will decrease to 40 years at 1.5°C and 2°C of global warming, and to only 21 years at 4°C.
Concerning heavy precipitation events and pluvial floods, there are no strong indications on past trends, due to high precipitation variability and lack of studies. However, there is high confidence that the intensity of extreme precipitation will increase in several parts of Africa, including the area affected by these floods.
The Congo basin holds the second largest rainforest in the world, and DRC is the second largest country by area of rainforest, between Brazil and Indonesia. While fossil fuel use in DRC is very low, in 2018 the country was the 12th largest CO2 emitter in the world, mainly due to land-use changes – primarily related to deforestation – linked to small-scale agriculture. Importantly, land-use change is not only a concurrent cause to global warming, but also a contributing factor to floods, as it impacts the hydrology of the terrain, leaving the soil exposed and vulnerable to erosion.
Our analysis approach rests on looking for weather conditions similar to those of the event of interest having been observed in the past. For this event, we have low confidence in the robustness of our approach given the available climate data, as the event is very exceptional in the data record. We are unable to account for the impact of human activities associated with land-use change and of changes in the human settlements. These factors help determine the vulnerability and the exposure of the territory to flood risk in presence of intense or exceptional precipitation events.
ClimaMeter Analysis
We analyze here (see Methodology for more details) how events similar to the meteorological conditions leading to the May 2025 Democratic Republic of the Congo floods have changed in the present (1987–2023) compared to what they would have looked like if they had occurred in the past (1950–1986) in the region [25°E 35°E 7°S 2°N]. Surface pressure changes show no relevant differences between the two periods. Temperature changes show increases of up to +0.75°C in the flood-affected area and up to +1.5°C in the surrounding regions. Precipitation changes reveal locally increased rain totals, with present-day conditions up to 7 mm/day wetter in parts of the Lake Tanganyika coast. No relevant wind changes are found in the region.
Similar past events show no relevant seasonal shift, with a slight increase in May compensated by a decrease in occurrence in June. Changes in urban areas reveal that Moba, Kalemie and Baraka experienced significantly wetter (+3 mm/day) and slightly warmer (+0.5 °C) conditions during this event compared to similar past conditions.
These results suggest that meteorological conditions similar to those of the May 2025 floods are becoming more favorable for precipitation, in line with what would be expected under continued global warming. Our results also suggest that sources of natural climate variability, such as the El Nino Southern Oscillation, may have played only a secondary role in shaping the observed event.
Conclusion
Based on the above, we conclude that meteorological conditions leading to the May 2025 floods in Democratic Republic of the Congo are up to 7 mm/day wetter (up to 25%) compared to similar past events. We interpret this event as an event driven by very exceptional meteorological conditions whose characteristics can mostly be ascribed to human driven climate change.
NB1: The following output is specifically intended for researchers and contain details that are fully understandable only by reading the methodology described in Faranda, D., Bourdin, S., Ginesta, M., Krouma, M., Noyelle, R., Pons, F., Yiou, P., and Messori, G.: A climate-change attribution retrospective of some impactful weather extremes of 2021, Weather Clim. Dynam., 3, 1311–1340, https://doi.org/10.5194/wcd-3-1311-2022, 2022.
NB2: Colorscales may vary from the ClimaMeter figure presented above.
The figure shows the average of surface pressure anomaly (msl) (a), average 2-meter temperatures anomalies (t2m) (e), cumulated total precipitation (tp) (i), and average wind-speed (wspd) in the period of the event. Average of the surface pressure analogs found in the counterfactual (b) and factual periods] (c), along with corresponding 2-meter temperatures (f, g), cumulated precipitation (j, k), and wind speed (n, o). Changes between present and past analogues are presented for surface pressure ∆slp (d), 2 meter temperatures ∆t2m (h), total precipitation ∆tp (i), and windspeed ∆wspd (p): color-filled areas indicate significant anomalies with respect to the bootstrap procedure. Violin plots for past (blue) and present (orange) periods for Quality Q analogs (q), Predictability Index D (r), Persistence Index Θ (s), and distribution of analogs in each month (t). Violin plots for past (blue) and present (orange) periods for ENSO (u), AMO (v) and PDO (w). Number of the Analogues occurring in each subperiod (blue) and linear trend (black). Values for the peak day of the extreme event are marked by a blue dot. Horizontal bars in panels (q,r,s,u,v,w) correspond to the mean (black) and median (red) of the distributions.