2024/04/20-23 China Floods

April 2024 China Floods exacerbated by both human-driven climate change and natural variability

Press Summary (First Published: 2024/04/25)

Event Description

From April 20th to April 23rd, the Guangdong province in China was gripped by severe flooding as major rivers, waterways, and reservoirs swelled dangerously, threatening the safety of over 127 million residents. Weather officials described the situation as "grim," with water levels in sections of rivers and tributaries at the Xijiang and Beijiang river basins reaching rare heights that only had a one-in-50 chance of occurring annually. China's water resource ministry issued an emergency advisory, prompting the government to enact emergency response plans to safeguard affected communities. Continuous heavy rainfall and severe convective weather exacerbated the flooding, leading to evacuations, power outages, and infrastructure damage in various cities such as Zhaoqing, Shaoguan, Qingyuan, and Jiangmen. Rescue efforts were underway, with authorities suspending maritime travel in certain areas and mobilising emergency resources. The situation was further compounded by similar weather-related challenges in neighbouring Guangxi province, where violent winds, hailstorms, and major flooding were reported, highlighting the widespread impact of the extreme weather event during that time period.

The Surface Pressure Anomalies show a large negative (cyclonic) anomaly over the western Guangdong province. The development of this depression was associated with the presence of cold air in the upper levels, providing instability, encouraging thunderstorms formation. Temperature anomalies show negative values over the area affected by heavy precipitation and positive anomalies in the rest of the analysed domain. Due to these conditions, the central Gongdong province saw the advection of large quantities of precipitable water from the ocean. As a consequence, Precipitation data show high amounts exceeding 60 mm/day over Shenzen and the surrounding area. Wind speed data show large areas of the domain affected by strong winds, with areas exceeding 50 km/h over the ocean.

Climate and Data Background for the Analysis

In the IPCC AR6 report, Chapter 12, states that heavy precipitation events in Asia will become more intense and frequent for a 2°C global warming or higher. This trend is given high confidence in all areas of Asia, except for the Arabian Peninsula, where medium confidence is assigned. These changes are marked by a projected decrease in the annual number of rain days, alongside with a shift in the seasonality of rainfall, with more intense and prolonged wet seasons. However, it is crucial to acknowledge that future projections exhibit substantial variability among different models and emission scenarios, underscoring the uncertainties that persist in predicting future extreme rainfall events in the region, as well as, their effects on the low-lying areas and densely populated coastline.

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 medium-high confidence in the robustness of our approach given the available climate data, as the event is similar to other past events in the data record

ClimaMeter Analysis

We analyze here (see Methodology for more details) how events similar to the pressure system leading to China Floods changed in the present (2001–2023) compared to what they would have looked like if they had occurred in the past (1979–2001) in the region [110°E 117°E 20°N 25°N]. The Surface Pressure Changes show that low pressure systems associated with similar events are slightly deeper in the present climate than what they would have been in the past over the region including China. The Temperature Changes show that similar events produce temperatures in the present climate that are up to 1 ºC warmer than what they would have been in the past, over the Northern part of the analysed region. The Precipitation Changes show wetter conditions (up to 6 mm/day) over the Southern domain. Windspeed Changes indicate no significant changes as compared to the past. We also note that Similar Past Events mostly occurred in similar months. Changes in Urban Areas reveal that Shenzhen is up to 5 mm/day wetter in the present compared to the past, while no significant changes are found for Qingyuan and Guangzhou.

Finally, we find that sources of natural climate variability, notably the Pacific Decadal Oscillation and the El Nino Southern Oscillation may have influenced the event. This suggests that the changes we see in the event compared to the past may be due to both human driven climate change and natural variability.


Based on the above, we conclude that depressions similar to those producing China Floods are 3-7 mm/day wetter in the present than they would have been in the past. We interpret China Floods as a somewhat uncommon event for which both human driven climate change and natural climate variability played a role.

Contact Authors

-Davide Faranda, IPSL-CNRS, France 📨davide.faranda@lsce.ipsl.fr 🗣️French, Italian, English

-Tommaso Alberti, INGV, Italy 📨tommaso.alberti@ingv.it 🗣️Italian, English

-Flavio Pons, IPSL, Italy 📨flavio.pons@lsce.ipsl.fr 🗣️Italian, English, French

-Gianmarco Mengaldo, NUS, Singapore 📨mpegim@nus.edu.sg 🗣️Italian, English

Additional Information : Complete Output of the Analysis

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.