September 2026 floods in Bangkok area likely influenced by both human-driven climate change and natural variability

Contact Authors

• Davide Faranda, IPSL-CNRS, France, davide.faranda@lsce.ipsl.fr

• Tommaso Alberti, INGV, Italy, tommaso.alberti@ingv.it

• Neven S. Fučkar, University of Oxford, neven.fuckar@ouce.ox.ac.uk 

• Haosu Tang, University of Sheffield, UK, haosu.tang@sheffield.ac.uk

Citation

• Faranda, D., Alberti, T., Fučkar, N. S., & Tang, H. (2026). September 2026 floods in Bangkok area likely influenced by both human-driven climate change and natural variability. ClimaMeter, Institut Pierre Simon Laplace, CNRS. https://doi.org/10.5281/zenodo.23083222

Press Summary

• Weather situations like the one driving the  September 2026 Bangkok floods now bring about 20-25% heavier rainfall and about 30% stronger winds over the Gulf of Thailand and its coasts than they would have in the past (1950-1987). 

• The event was associated with a highly exceptional weather pattern, characterized by a persistent low pressure system over central Thailand. As a result, we have low confidence in the attribution statement.

• Approximately 14 million people and 134 billion USD  in economic activity were exposed within the area where rainfall has intensified. Of the resulting rain-linked damages, an estimated 5% is attributable to human-driven climate change.

• We find that both human-driven climate change and natural climate variability contributed to the event.

Figure 1. ClimaMeter analysis of the Bangkok floods (25 September 2026, the first full day of the heavy rain), over central Thailand, the Gulf of Thailand and the Andaman coast. (a, b) circulation (sea-level pressure) and temperature anomalies; (c) precipitation and (d) wind speed during the event; (e, f, g, h) the present-minus-past changes in circulation, temperature, precipitation and wind speed, coloured only where statistically significant; (i) the months in which similar past events occurred; (j) changes at the selected cities (Bangkok and Chon Buri), with non-significant changes set to zero. The two dials summarise the attribution (left) and how rare the pattern is (right).

Event Description

Heavy, almost continuous rain fell on Bangkok and central Thailand from the afternoon of Thursday 24 September 2026, with close to 300 mm recorded across the capital in roughly 48 hours. Main roads were flooded and residents evacuated, and on 26 September the authorities declared all 50 districts of Bangkok a disaster zone. Schools were closed, employees were asked to work from home, and the government approved 28 and 29 September as special holidays for Bangkok and the surrounding provinces due to the disruption. The event became the worst flooding in the capital in fifteen years with cascading economic impacts, including disruption at Bangkok airports.

By 29 September, flooding affected about 2.6 million people in more than 940,000 households across 29 provinces and all 50 districts of Bangkok. The death toll rose to 23, including eight in Samut Prakan, six in Sa Kaeo and four in Bangkok, and the government approved emergency funding for the affected areas. The Royal Irrigation Department increased the discharge from the Chao Phraya Dam and warned residents of potential floods in downstream provinces along the Chao Phraya and Noi rivers.

The rain was caused not by a tropical cyclone, but by a persistent monsoon low pressure system. The peculiarity of this weather system is its slow movement: meteorological reports indicate that the low remained over central Thailand for much of 25–26 September, enabling prolonged convection over approximately the same region rather than quickly passing through. 

To capture the onset of the rain, the analysis uses the single day of 25 September 2026, the first full day of heavy rainfall, over a domain covering central Thailand, the Gulf of Thailand and the Andaman coast. The Circulation Anomalies panel (Fig. 1a) shows a low-pressure anomaly of about 2 to 3 hPa centred over central Thailand, north of Bangkok with cooler-than-normal temperature anomalies (Fig. 1b). The Precipitation Data panel (Fig. 1c) shows a band of heavy rain, locally above 150 mm/day, from central Thailand south-east through Bangkok towards Chon Buri, and sustained winds up to 50 km/h (Fig. 1d)

A Summary of How ClimaMeter Works

ClimaMeter attributes an event by comparing it with its closest historical analogues, that is, past days whose large-scale weather pattern most closely resembles the event. The analogues are split into a past period (1950 to 1987) and a present period (1988 to 2025); the difference between the two sets shows how weather situations like this one have changed as the climate has warmed. All fields are detrended and deseasonalised before the search, and the significance of the changes is assessed with a 1000-member bootstrap. To prevent several consecutive days of the same episode from being counted as separate analogues, the retained analogues are required to be at least seven days apart. The two dials summarise, on the left, how much of the change is due to human-driven climate change versus natural variability, and, on the right, how rare the meteorological pattern is in the historical record.

Climate and Data Background for the Analysis

The IPCC Sixth Assessment Report (AR6, Working Group I, Chapter 11) concludes that heavy precipitation has become more frequent and intense over most land regions with good observational coverage, and that human-driven warming is the main driver, since a warmer atmosphere holds about 7% more moisture per degree of warming. For Southeast Asia, confidence in observed trends is lower because of sparse long-term records and strong year-to-year monsoon variability, but further increases in extreme rainfall are projected with continued warming. Working Group II, Chapter 10 (Asia) identifies large, low-lying delta cities such as Bangkok, where land subsidence, rapid urbanisation and river flooding combine, as among the most exposed to flood risk in Asia. 

ClimaMeter Analysis

We analyse how weather situations similar to the September 2026 Bangkok floods have changed between the past (1950 to 1987) and the present (1988 to 2025) over central Thailand and the surrounding seas, identifying analogues from the sea-level pressure pattern. The Precipitation Changes (Fig. 1g) are statistically significant over most of the Gulf of Thailand and its coasts: there, analogous situations now bring about 20-25% heavier rainfall than in the past (relative to the event rainfall), with local increases of more than 10 mm/day along the eastern shores of the Gulf, towards Chanthaburi and Trat, and along the Andaman coast of Myanmar. Temperature changes for analogous situations are about 0.5°C higher (Fig. 1f), up to about 1°C over central and northern Thailand, including a significant warming of about 0.8°C in Bangkok and 0.7°C in Chon Buri (Fig. 1j). Wind speeds over the Gulf of Thailand are about 30% stronger for analogous situations (Fig. 1h). Finally, we find that sources of natural climate variability, notably the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation, may have influenced the event. This suggests that the changes we see in the event compared to the past may be partly due to human-driven climate change, with a contribution from natural variability. Similar Past Events (Fig. 1i) cluster in October, with present-day analogues occurring more often in September and past ones more often in October. For this event, the right-hand dial in Figure 1 points to a very exceptional weather pattern, with few close analogues in the 1950 to 2025 record, so the analogue-based attribution carries low confidence.

Exposure

To estimate the population and economic assets exposed to climate change-influenced conditions, we overlay the ClimaMeter rainfall-hazard map onto gridded datasets of population density and economic activity using the methodology developed in Faranda et al 2026 (ERL). The hazard map retains only the grid cells where present-day conditions during events like the studied one are significantly more extreme than in the historical baseline (1950 to 1987). Onto this area, we overlay gridded population and gridded gross domestic product both at a spatial resolution of about 50 km.

We then aggregate the population and economic value within the hazard area and categorize them into three severity classes. The categories are based on how unusual the local rainfall was during the event relative to the 1950-2025 record:

• Moderate: falling between the local 98th and 99th percentiles (representing the most extreme one to two per cent of days);

• Severe: falling between the 99th and 99.5th percentiles (the most extreme half to one per cent); and

• Extreme: exceeding the 99.5th percentile (the rarest and most intense conditions).

Importantly, these figures measure exposure, the people and assets in harm’s way potentially impacted, not the realized losses.

About 14.4 million people and 134 billion USD of economic activity fall within the rainfall-hazard area, out of a domain total of about 118 million people and 1.6 trillion USD. By severity class, roughly 4.6 million people are in the moderate class, 2.8 million in the severe class and 7.0 million in the extreme class. In economic terms, about 22 billion USD are in the moderate class, 13 billion USD in the severe class and 99 billion USD in the extreme class, so the exposure is dominated by the most intense rainfall (Fig. S4).

Impacts and Vulnerability

The methodology for calculating the climate-linked damage share is adapted from Faranda et al. 2026 (QJRMS). In this case, each grid cell represents the geographical spread of the event. We first calculate how much more intense the hazard is today compared to historical analogues, defined as the present-minus-past difference of the analogue composites. Afterwards, we restrict this estimate to the hazard core, representing the cells where the event was most severe and where the change is statistically significant. Applying this relative intensification to the exposed population and assets splits the local impact into two components that sum to 100 per cent in every affected cell: (1) a baseline share, which the weather extreme would have caused anyway, and (2) a climate-change share, the part intensified by the human-driven warming. Because these results are fractions independent of absolute monetary losses, we report percentages only, while the values below are estimates.

We quantify the uncertainty on this attributable share from the analogue bootstrap. For every grid cell the present-minus-past change (the delta) carries a distribution whose spread is given by the 1000-member bootstrap. We take the 10th and 90th percentiles of that per-cell delta distribution and recompute the climate-attributable damage for each, bracketing the central estimate. Because the low and high percentiles are applied to every cell at once, the resulting range (reported in brackets) is a conservative envelope of the uncertainty in the attributed hazard change rather than a strict confidence interval.

Aggregated over the exposed assets, about 5% (10-90th percentile 3.5 to 7%) of the rain-linked damage is attributable to human-driven climate change (Fig. S5). Crucially, this percentage represents the climate-driven fraction of realized losses, rather than a proportion of the total exposed assets. River flooding along the Chao Phraya, drainage capacity and land subsidence, which strongly shape flood damage in Bangkok, are not represented in this rainfall-based estimate.

Conclusion

Weather situations similar to the September 2026 Bangkok area floods now bring about 20-25% heavier rainfall and about 30% stronger winds over the Gulf of Thailand and its coasts, and temperatures about 0.5°C higher, than in the past (1950 to 1987). About 14 million people and 134 billion USD of economic activity lie within the rainfall-hazard intensified area, and about 5% (10-90th percentile 3.5 to 7%) of the rain-linked damage is attributable to human-driven climate change. Because the weather pattern is very exceptional, this attribution carries low confidence. Finally, we find that sources of natural climate variability, notably the AMO and the PDO, may have influenced the event. This suggests that the changes we see in the event compared to the past may be partly due to human-driven climate change, with a contribution from natural variability.