Contact Authors
Tommaso Alberti, INGV, Italy 📨tommaso.alberti@ingv.it 🗣️English, Italian
Davide Faranda, IPSL-CNRS, France 📨davide.faranda@lsce.ipsl.fr 🗣️English,French, Italian
Valerio Lucarini, University of Leicester, UK 📨v.lucarini@leicester.ac.uk 🗣️English,, Italian
Gianmarco Mengaldo, National University of Singapore,Singapore 📨mpegim@nus.edu.sg 🗣️English, Italian
Citation
Alberti, T., Faranda, D., Lucarini, V., & Mengaldo, G. (2025). Heavy rain in November 2025 Indonesian floods intensified by human-driven climate change. ClimaMeter, Institut Pierre Simon Laplace, CNRS. https://doi.org/10.5281/zenodo.17817196
Press Summary
Meteorological conditions similar to those causing the Indonesian floods are up to 7 mm/day (about +10%) wetter in the present than they have been in the past.
This event was associated with exceptional meteorological conditions.
We ascribe the heavier precipitation of November 2025 Indonesian floods to human-driven climate change.
Event Description
At the end of November 2025, Indonesia’s island of Sumatra was devastated by exceptional monsoon rains and a rare tropical storm — causing massive flooding, flash floods, and widespread landslides across provinces including West Sumatra, North Sumatra and Aceh. According to the national disaster‑management agency, the death toll initially reported at 753 was later revised to 708 confirmed fatalities, with 504 people still missing. More than 3.2–3.3 million people have been affected; over 1 million people were evacuated from high‑risk zones. Thousands of homes — in many cases entire villages — were submerged or destroyed; farmland, shops and local infrastructure suffered catastrophic damage. Critical infrastructure like roads, bridges and communication lines were washed away or buried, isolating towns and complicating rescue and relief operations. Widespread deforestation, logging and unregulated land‑use have greatly worsened the impact of the storm — removing natural buffers, making soil more vulnerable to erosion, landslides and flooding, and turning seasonal rains into full‑blown disasters.
The meteorological conditions were characterized by negative surface-pressure anomalies, with values down to about –2 hPa over northern Indonesia, indicating a shallow but persistent low-pressure environment. Temperatures show positive and negative contrasts reaching about -1 °C northward to Sumatra and about +1 °C over Malesia. Precipitation locally exceeded 100 mm/day, with intense rainfall concentrated over the coast, especially close to Medan. Wind speeds during the event reached 40 km/h, with a pronounced northward flow clearly enhancing moisture advection and contributing to sustained, heavy rainfall over the region.
Climate and Data Background for the Analysis
The IPCC AR6 report projects that in Southeast Asia, both mean monsoon precipitation and pluvial flooding are expected to increase, with greater intensity and frequency of heavy rainfall events as global warming progresses. These changes are attributed to the increased atmospheric moisture content under warming and amplified convective activity. Additionally, reduction of atmospheric aerosols associated with improvements in pollution management standards are also expected to contribute to the strengthening of the precipitation by reducing atmospheric stratification. Southeast Asia is identified in the AR6 WGI Summary for Policymakers as a region where heavy precipitation events will intensify and become more frequent.
Observational studies over the Indonesian Maritime Continent — including Bali — have documented upward trends in extreme rainfall indices, especially in response to intraseasonal variability like the Madden-Julian Oscillation (MJO). During active MJO phases, western and central Indonesia can experience up to 70–80% increases in extreme precipitation probability. Embedded in a climate system that is becoming wetter, climate variability contributes to the intensification of extreme events. The Indian Ocean Dipole (IOD) and El Nino–Southern Oscillation (ENSO) also modulate rainfall variability across Indonesia, including Bali, by influencing moisture advection and convection dynamics. Under anthropogenic warming, the Indo-Pacific warm pool has been expanding and warming, which strengthens moisture fluxes and amplifies extreme precipitation potential over maritime Southeast Asia.
Our analysis approach rests on looking for weather situations 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.
ClimaMeter Analysis
We analyze here (see Methodology for more details) how events similar to the meteorological conditions leading to the November 2025 Indonesia 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 [96°E–104°E, 0°N–5°N]. Surface-pressure changes do not display significant changes. Temperature changes show increases of up to +1.0 °C over all the region, indicating a warmer background state at the time of the 2025 event. Precipitation changes reveal a general increase in rainfall intensity: analogous weather situations are now up to 7 mm/day wetter than comparable events in the past — corresponding to roughly +10% in precipitation intensity under present-day conditions compared to the 1950–1986 baseline. Wind-speed changes show enhanced wind-speeds of up to +4 km/h over coastal or moisture-advection zones, favoring stronger moisture transport from the adjacent seas into the inner region of Sumatra.
Similar past events display a seasonal shift from October in the past to November and December in the present. Changes in urban areas of Sibolga and Tarutung reveal precipitation increases up to 7 mm/day compared to similar past situations, increasing vulnerability to flooding given contemporary land use and development. These results suggest that meteorological conditions similar to those of the November 2025 floods in Vietnam are becoming more favorable for extreme precipitation events — consistent with expectations under continuing global warming.
Finally, we find that sources of natural climate variability may have not influenced the event. This suggests that the changes we see in the event compared to the past are mainly due to human driven climate change. We underline that, following the protocol used for our analysis, we did not use the IOD index which may have influenced the event.
Conclusion
Based on the above, we conclude that meteorological conditions leading to the Indonesia floods are more intense with up to 2 hPa deeper, up to 7 mm/day (10%) wetter in the present, and up to 4 km/h (10%) windier over the coasts than they would have been in the past. We interpret Indonesia floods as an event driven by very exceptional meteorological conditions whose characteristics can 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 [1979-2000] (b) and factual periods [2001-2022] (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.