Extreme Rainfall Research Articles

The Impacts of Convectively Coupled Equatorial Waves on Extreme Rainfall in Northern Australia

Abstract Convectively coupled equatorial waves (CCEWs) with an off-equatorial convective center, such as equatorial Rossby (ER) waves, mixed Rossby–gravity (MRG) waves, and tropical depression (TD)-type waves, can be the potential sources of predictability for subseasonal to seasonal prediction over northern Australia. To establish the statistical relationship of the wave–rainfall interaction, we investigate the influences of CCEWs on rainfall means and extremes during the austral summer (December–February) and autumn (March–May) from 1981 to 2018. The results show that ER waves increase the average daily rainfall by up to 7 mm day−1 (4 mm day−1) during the austral summer (autumn) and increase the probability of extreme rainfall (above the 90th percentile) by around 1.5–2.4 times (summer) and 1.1–1.8 times (autumn) relative to climatology. MRG and TD-type waves are shown to have a smaller impact, increasing rainfall by around 1–4 mm day−1 (1–1.5 and 1–3 mm day−1) during the summer (autumn) and extreme probability by 1.4–1.6 times and 1.25–1.9 times (1.3–1.8 times and 1.27–1.7 times), respectively. The increase in rainfall can be attributed to the enhancement of moisture convergence during the time of the rainfall. Furthermore, moisture gain and enhancement of moisture advection were found ahead of the convective center. Additionally, we find that interactions between multiple waves can act to amplify or suppress the mean daily and probability of extreme rainfall. This research highlights the important role of CCEWs on northern Australian precipitation variability. Significance Statement This research is the first to delve into the significant role of convectively coupled equatorial waves (CCEWs) on subseasonal rainfall variability over northern Australia, particularly equatorial Rossby (ER) waves, mixed Rossby–gravity (MRG) waves, and tropical-depression-type (TD-type) waves. A robust statistical relationship is found between these waves and rainfall, suggesting these waves are major contributors to increased daily rainfall and extreme rainfall probabilities during austral summer and autumn. These findings emphasize the significance of CCEWs on northern Australian rainfall variability and highlight the potential of CCEWs for subseasonal to seasonal forecasts in Australia.

Future changes in rainfall variability for the mainland Indochina southwest monsoon

Researching future changes in rainfall variability is critical for mitigating the possible effects of global warming, especially in areas where vulnerability is high, such as Indochina. While changes in mean and extreme rainfall have received a great deal of attention, rainfall variability has been poorly researched, despite its importance. We endeavored to determine the anticipated changes in rainfall variability during the mainland Indochina southwest monsoon (MSWM) by utilizing data derived from 5 ensemble models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Employing band pass filtering techniques on daily rainfall data, we discerned variability across an expansive spectrum of temporal scales. Our research indicates that, in the event of global warming, the variability in MSWM rainfall is expected to increase by approximately 10-25% throughout the whole region. Notably, this increased unpredictability appears uniformly throughout a wide range of time intervals. Changes in average rainfall significantly aid in explaining the majority of the intermodal variance in the predicted MSWM rainfall variability. To gain further insight into this phenomenon, we examined the effects of elevated atmospheric moisture content through the estimation of modifications resulting from idealized local thermodynamic enhancement. We showed that increased atmospheric moisture, as suggested by the Clausius-Clapeyron relation, accounts for most of the predicted changes in rainfall variability at all time scales.

Trends in extreme rainfall over the past 55 years suggest springtime subhourly rainfall extremes have intensified in Mahantango Creek, Pennsylvania.

Extreme short-duration rainfall is intensifying with climate warming, and growing evidence suggests that subhourly rainfall extremes are increasing faster than more widely studied durations at hourly and daily timescales. In this case study, we used 55 years (1968-2022) of 5-min precipitation data from Mahantango Creek, a long-term experimental agricultural watershed in east-central Pennsylvania, United States, to examine annual and seasonal changes in subhourly (15-min), hourly, and daily rainfall extremes. Specifically, we evaluated temporal trends in the magnitude and frequency of subhourly, hourly, and daily rainfall extremes. We then estimated apparent scaling rates between rainfall extremes and dew point temperature (Td) and compared these rates to the Clausius-Clapeyron (CC) rate (∼ 7% per °C). We also determined the coincidence of extreme rainfall trends with indicators of atmospheric instability and convective-type precipitation. Overall, we found the most significant changes in rainfall extremes at 15-min durations during the spring, with magnitudes of these subhourly extremes increasing by 0.6 to 0.9% per year, and frequencies rising by 3.4% per year. Apparent scaling rates in the spring showed that 15-min rainfall extremes transitioned from sub-CC scaling to greater than 2CC scaling when Td reached 11° C, implying a possible shift from stratiform rains to more intense convective rains above this Td threshold. Notably, trends in maximum hourly convective available potential energy (CAPE) increased during spring, as did the ratio of 15-min rainfall extremes to their corresponding daily rainfall totals. Findings indicate that convective-type precipitation may be playing an increasing role in the intensification of springtime 15-min rainfall extremes in Mahantango Creek.

Extreme Rainfall Analysis in Pernambuco, Northeast Brazil, Using a High‐Resolution Gridded Dataset

ABSTRACTThis paper presents a detailed spatio‐temporal analysis of the rainfall in the state of Pernambuco, Northeast Brazil. It is based on climate indices for extreme precipitation recommended by the Expert Team on Climate Change Detection, Monitoring and Indices. To accomplish this, daily rainfall 1data (1961–2019) were extracted from 809 high‐resolution grid points (0.1° × 0.1°) using the Brazilian Daily Weather Gridded Data (BR‐DWGD). The significance and magnitude of index trends were assessed using the modified Mann–Kendall and Sen's slope tests. This study also examined whether there existed a significant difference in climate indices among the three regions (Sertão, Agreste and Zona da Mata) within the state. The findings revealed notable significant negative trends in the PRCPTOT, R10mm, R20mm, Rx1day, Rx5day and CWD indices across all regions of Pernambuco, exhibiting a gradient from the coast to the state's interior. Reduction values of up to 15 mm year−1 for PRCPTOT, 0.7 day year−1 for R10mm, 0.2 day year−1 for R20mm, 0.01 mm year−1 for Rx1day, 0.03 mm year−1 for Rx5day, 0.4 day year−1 for CWD were observed. Furthermore, an alarming pattern was also noted for CDD, displaying a higher concentration of significant positive trends in all regions of the state, with estimated increases of up to 1.4 day year−1. Conversely, a balance of trends—both positive and negative—was observed across the entire state for R95p and R99p, with a majority of trends proving non‐significant. SDII exhibited a higher frequency of grid points showing a significant positive trend, particularly notable in the Sertão and Zona da Mata regions, where significant differences in the index values were absent. However, the remaining indices showcased notable regional differences, with values decreasing from the east to the west of the state, except for CDD. This study will assist decision makers, providing detailed long‐term information essential for preventing natural disasters and supporting socioeconomic and environmental policies in the state.

Characterization of dam failure flooding in an urban reservoir under different rainfall conditions.

In water conservation projects, reservoirs play a key role in controlling and regulating floods. In recent years, extreme rainfall events have occurred more frequently. Urban reservoirs in danger under extreme rainfall happen occasionally. The breaching of urban reservoirs seriously threatens the lives and property of the population downstream. A lack of clarity regarding the characterization of the flooding response remains owing to urban reservoir overtopping under different rainfall conditions. This study used observed data from the heavy rainfall event in Zhengzhou on July 20, 2021, with Jiangang Reservoir in Zhengzhou as the study site. The study designed a storm-flood process with different rainfall scenarios that combines the long-duration rainstorm formula and Chicago rain pattern method, establishing a response relationship between extreme rainfall and flooding processes. Flood evolution simulation analyses were carried out for different scenarios of rainfall and flooding processes under three working conditions: rainfall only, dam failure only and dam failure coupled with rainfall, to characterize the flood response due to dam failure under different rainfall conditions. The results showed that the peak of the rainfall occurs earlier in the total rainfall calendar time, the faster the dam failure rate of the reservoir. As the rain peak occurs at a time that is later in the total rainfall calendar, and the recurrence period and rainfall duration increase, the flood volume increases and urban inundation becomes more severe. The rainfall type had less of an effect on the difference in the extent of inundation between different working conditions. The extent of flood inundation for the dam failure-coupled rainfall scenario is much greater than that for the dam failure only scenario. The increase in inundation extent in the dam failure coupled rainfall scenario at the higher levels of flood severity is smaller. Study results provide hydraulic element support for flood hazard assessment.

Muddy Waters: Design Thinking for Understanding the Multi-Organizational Problem Space of the Water Sector

Context and motivation: Climate change is manifested by climate variability, rising temperatures (and thus evaporation), and extreme events such as droughts and floods, which have a profound effect on the availability of natural resources, for example, high-quality water. While several technologies for addressing these challenges are available, their adoption is not widespread. In this study, a design thinking (DT) approach was applied to understand the problem space of floods and their handling by the Israeli water sector. Specifically, we aim at addressing the following question: What are the gaps in and barriers to adopting solutions that address sewerage flooding during extreme heavy rainfall events? The DT approach exposed major problems in the conduct of the water sector, including a lack of communication among organizations, the ill-defined distribution of responsibility, unclear and conflicting guidance, and insufficient funds and technological solutions, all hindering the possibility of adopting an integrative solution. This study demonstrates the role that DT plays in understanding a complex, multi-organizational problem space, in our case, the climate change readiness of the water sector, before delving into technological development. Any solution development should involve participants from the various organizations involved in the challenge. It is vital to address not only each organization’s requirements but also its technology adoption barriers and to initiate a comprehensive discussion, ultimately resulting in a shared understanding of all the facets of the challenge that can impact solution development and deployment.

Application of Capillary Barrier Systems for Slope Stabilization Under Extreme Rainfall: A Case Study of National Highway 10, India

Global warming has led to an increase in extreme rainfall events, which often result in landslides, posing significant threats to infrastructure and human life. This study evaluated the effectiveness of the Capillary Barrier System (CBS) in enhancing slope stability along a vulnerable section of India’s National Highway 10 (NH10) during maximum daily rainfall. The GEOtop model was employed to conduct water balance simulations and obtain the pore–water pressure (PWP), which was then used to calculate the Factor of Safety (FoS). Results showed that CBS effectively delayed the rise in PWP, leading to lower peak values and smaller areas of very high and high risk levels. Spatial distribution mapping further confirmed that CBS minimized very high risk zones. At three historical landslide points, CBS slopes generally maintained FoS values above 1, demonstrating enhanced stability and improved resilience to extreme rainfall. These findings highlight the potential of CBS as a viable strategy for slope reinforcement in regions susceptible to heavy rainfall.

How climate change intensified storm Boris’ extreme rainfall, revealed by near-real-time storylines

Disentangling the impact of climate change on environmental extremes is of key importance for mitigation and adaptation. Here we present an automated system that unveils the climate change signal of the day in near-real-time, employing a set of innovative storyline simulations based on a coupled climate model. Its potential to complement probabilistic assessments is showcased for storm Boris, which brought record-breaking rainfall over Central and Eastern Europe in September 2024, leading to devastating floods. Our near-real-time storylines suggest that storm Boris deposited about 9% more rain due to human-induced warming. The area impacted by the same storm’s extreme rainfall (>100 mm) was 18% larger and would continue expanding in a future warmer climate. Results from our prototype storyline system are disseminated publicly via an online tool. The case of Storm Boris demonstrates the potential of near-real-time storylines for rapid evidence-based climate change communication.

The North China record-breaking rainfall in July 2021: The atmospheric influential factors and precursory signal

Abstract In July 2021, the southeastern part of North China (SENC) suffered a record-breaking extreme rainfall event that caused devastating flooding and enormous losses. In this study, the major atmospheric influential factors and the precursory signal of heavy rainfall in 2021 are investigated using the correlation, regression, power spectrum, and filtering methods, the quasi-geostrophic velocity equation, observational data and numerical simulation of a linear baroclinic model (LBM). The results show that the extremity of a quasi-barotropic high anomaly over Northeast Asia (NEA) contributes to the deep anomalous upward motion within SENC by inducing positive vorticity and temperature advections. On the other hand, the anomalous southeasterly flow at the southwestern flank of the NEA high anomaly transports sufficient moisture to SENC in the lower troposphere. The local deep upward motion combined with the lower-tropospheric moisture convergence directly leads to the occurrence of this extreme rainfall event. Further analysis shows that the intensification of the NEA high in July 2021 is closely tied to the westward migration of atmospheric disturbance originating from the vicinity of Northeast Pacific-North America, which could be supported by numerical simulation in LBM. The variation of the geopotential height anomaly over Northeast Pacific-North America precedes that of the NEA high by two weeks, which is likely to provide a potential source of predictability for the extreme rainfall in SENC.

Global record-breaking recurrence rates indicate more widespread and intense surface air temperature and precipitation extremes.

We analyzed the evolution of extreme annual surface air temperature and rainfall on Earth, based on the recurrence rate of record-breaking events, and found the highest recurrence rates for record-high annual temperatures in the tropics, as opposed to the polar regions with the fastest warming. Both recurrence rates and the global surface area fraction with daily mean surface air temperatures exceeding 30° and 40°C provide further evidence for extremely hot years becoming more common and widespread. A similar analysis for precipitation highlighted some regions with more record-high annual total precipitation and others with record-low annual precipitation typically associated with drought. A multimodel ensemble of 306 runs with global climate models [Coupled Model Intercomparison Project phase 6 (CMIP) Shared Socioeconomic Pathways 2-45 (SSP2-45)] reproduced the statistics of record-breaking high temperatures, but there were some differences for the reanalysis precipitation record-breaking recurrence rates. The global climate model simulations suggested a slightly altered geographical pattern for record-breaking annual precipitation recurrence rates.

Role of Quasi‐Biweekly Cloud‐Radiative Feedback in Modulating and Simulating Extreme Rainfall Intensity Over Asian Monsoon Regions

AbstractAlthough Cloud‐radiative feedback (CRF) has been investigated in terms of its modulation of extreme precipitation over tropical oceans, the specific timeframe of CRF variability and the associated processes driving extreme rainfall over Asian monsoon regions remain unclear. Based on observational analyses and model sensitivity experiments, this study reveals that longwave CRF at the quasi‐biweekly timescale is most closely linked to the intensity of persistent (≥3‐day) heavy rainfall events in southern China and northern India by efficiently maintaining the large‐scale convective perturbations. When CRF is disabled, the quasi‐biweekly convective anomalies supporting extreme precipitation are weakened. Additional assessments of Coupled Model Intercomparison Project historical simulations confirm that models with more accurate quasi‐biweekly longwave CRF exhibit smaller biases in capturing the amplitude of persistent heavy rainfall. These results have implications for improving model capability in simulating and forecasting quasi‐biweekly CRF to mitigate the risks of extreme precipitation events in monsoonal Asia.

Differential flood insurance participation and housing market trajectories under future coastal flooding in the United States

Communities respond to flooding events based upon risk perceptions and available adaptive behaviors (e.g., emigrating, purchasing insurance, constructing levees). Across the United States, sea level rise, intensifying storm-surges, and extreme rainfall may alter human-flood dynamics. Here, we use calibrated Socio-Environmental models of contiguous US coastal census tracts and two shared socio-economic pathways (SSP245 and SSP585). We project that by 2100, total flood insurance claims will increase by +25% to +130% under low (SSP245) and high (SSP585) emissions scenarios, respectively. The increase in flood insurance claims will impact mainly socially vulnerable communities. Further, we project that active NFIP policies will increase from +30% under low emission scenario to +60% under high emission scenario. Our finding also suggests the growing debt of the National Flood Insurance Program under higher emissions. Raising the water elevation threshold for coastal flooding by +1 meter via levees may reduce future surge-related losses by 95% and 40% under low and high emission scenarios and stabilize housing markets. Our future projections of flood insurance claims, policy coverage, and the impact of water elevation serve as credible hypotheses for the evolution of human flood dynamics under climate change. They can inform national flood policy and future research.

New perspectives on urban stormwater management in China, with a focus on extreme rainfall events

New perspectives on urban stormwater management in China, with a focus on extreme rainfall events

Estimating Rainfall Anomalies with IMERG Satellite Data: Access via the IPE Web Application

This study assesses the possibilities of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) to estimate extreme rainfall anomalies. A web application, the IMERG Precipitation Extractor (IPE), was developed which allows for the querying, visualization, and downloading of time-series satellite precipitation data for points, watersheds, country extents, and digitized areas. The tool supports different temporal resolutions ranging from 30 min to 1 week and facilitates advanced analyses such as anomaly detection and storm tracking, an important component for climate change study. To validate the IMERG precipitation data for anomaly estimation over a 22-year period (2001 to 2022), the Rainfall Anomaly Index (RAI) was calculated and compared with RAI data from 2360 NOAA stations across the conterminous United States (CONUS), considering both dry and wet climate regions. In the dry region, the results showed an average correlation coefficient (CC) of 0.94, a percentage relative bias (PRB) of −22.32%, a root mean square error (RMSE) of 0.96, a mean bias ratio (MBR) of 0.74, a Nash–Sutcliffe Efficiency (NSE) of 0.80, and a Kling–Gupta Efficiency (KGE) of 0.52. In the wet region, the average CC of 0.93, PRB of 24.82%, RMSE of 0.96, MBR of 0.79, NSE of 0.80, and KGE of 0.18 were computed. Median RAI indices from both the IMERG and NOAA indicated an increase in rainfall intensity and frequency since 2010, highlighting growing concerns about climate change. The study suggests that IMERG data can serve as a valuable alternative for modeling extreme rainfall anomalies in data-scarce areas, noting its possibilities, limitations, and uncertainties. The IPE web application also offers a platform for extending research beyond CONUS and advocating for further global climate change studies.

Using human observations with instrument-based metrics to understand changing rainfall patterns.

Shifting precipitation regimes are a well-documented and pervasive consequence of climate change. Subsistence-oriented communities worldwide can identify changes in rainfall patterns that most affect their lives. Here we scrutinize the importance of human-based rainfall observations (collated through a literature review spanning from 1994 to 2013) as climate metrics and the relevance of instrument-based precipitation indices to subsistence activities. For comparable time periods (1955-2005), changes observed by humans match well with instrumental records at same locations for well-established indices of rainfall (72% match), drought (76%), and extreme rainfall (81%), demonstrating that we can bring together human and instrumental observations. Many communities (1114 out of 1827) further identify increased variability and unpredictability in the start, end, and continuity of rainy seasons, all of which disrupt the cropping calendar, particularly in the Tropics. These changes in rainfall patterns and predictability are not fully captured by existing indices, and their social-ecological impacts are still understudied.

The effect of day-to-day temperature variability on agricultural productivity

Abstract With rising extreme weather events due to climate change, the impact on agricultural production has become increasingly severe. Yet, there has been a significant gap in research that assesses the influence of day-to-day temperature variability on agricultural productivity on a global scale. Our study addresses this gap by exploring the effects of day-to-day temperature variability and the change of rainfall patterns on agricultural productivity worldwide from 1961 to 2018. The results reveal that day-to-day temperature variability not only has a direct, negative impact on agricultural Total Factor Productivity (TFP), but also influences it by modulating the effects of monthly average temperatures and wet days. One unit increase of day-to-day temperature variability leads to a 2% decrease in TFP. Day-to-day temperature variability neutralizes the impact of monthly average temperature on TFP, while exacerbating the impact of wet days on TFP. Furthermore, extreme rainfall events result in a consistent negative marginal effect across all countries/seasons/rainfall intervals. This study also identifies differentiated impacts across countries with varying income levels. Low-income regions’ TFP demonstrates markedly significant sensitivities to both monthly average temperatures and daily temperature fluctuations, which means less resilient. Furthermore, the impacts of general and extreme rainfall are comparatively less pronounced in high-income countries, indicating higher resilience to climate variability in these regions and a relative vulnerability to extreme weather events in low-income regions. Our findings illuminate the intricate and multifaceted role that daily temperature variability plays in agricultural productivity, providing a theoretical basis for understanding the heterogeneous impacts of climate change on agriculture and contributing insights into the broader discourse on climate resilience and agricultural sustainability.

Environmental critical thresholds based on statistical analysis for modelling landslide susceptibility in Continental Basaltic Provinces

Abstract. The study aims to estimate the environmental critical thresholds using statistical approaches to understand the landslide conditioning factors that can trigger landslides in the Continental Basaltic Provinces a landslide-prone area, using as reference the landslides that occurred in an extreme rainfall event. The study area is a region that was the scene of an extreme hydrological event in January 2017, with an accumulated volume of rain of 163.9 mm in 8 hours, causing a widespread event of shallow planar landslides with more than 400 scars detected. Hydrological, anthropic, geological, geomorphological, and topographical features of this region were analyzed considering landslides and non-landslides samples set, and their influence in the event was carried out using the Frequency Ratio method, followed by Pearson's Linear Correlation Coefficient and Linear Regression. The results showed that this process helped us to understand environmental critical thresholds based on classes of conditioning factors that have a greater influence on rainfall-triggered landslide occurrences and, consequently, higher predictive capacity in the landslide susceptibility models with the same geoenvironmental parameters which is a valuable insight for risk management.

Sub-daily precipitation returns levels in ungauged locations: Added value of combining observations with convection permitting simulations

Extreme rainfall events trigger natural hazards, including floods and debris flows, posing serious threats to society and the economy. Accurately quantifying extreme rainfall return levels in ungauged locations is crucial for improving flood protection infrastructure and mitigating water-related risks. This paper quantifies the added value of combining rainfall observations with Convection Permitting Model (CPM) simulations to estimate sub-daily extreme rainfall return levels in ungauged locations. We assess the performance of CPM-informed estimates of extreme return level against a traditional interpolation techniques. We find that kriging methods with external drift outperform inverse distance weighting for both traditional and CPM-informed approaches. We then assess the effectiveness of the two methods under different scenarios of station density. At the highest station density (1/196 km²), traditional interpolation methods outperform the CPM-informed method for durations under 6 h. The performance becomes comparable between 6 and 24 h. For lower station densities (1/400 and 1/800 km²), the CPM-informed method outperforms the traditional method, with average reductions in fractional standard error of 24 %, 13 %, and 8 % for return periods of 2, 10 and 50 years, respectively for a rain gauge density of 1/800 km², and 16 %, 8 %, and 3 % for density of 1/400 km². Information from CPM simulations can thus be useful for estimating sub-daily extreme rainfall events in ungauged sites, particularly in data-scarce areas in which the density of rain gauges is low.

Spatiotemporal Pattern Detection of Ground Deformations Induced by Extreme Rainfall using InSAR EGMS: The case of Cortina d’Ampezzo after Vaia Storm

Abstract. During October 2018 Vaia Storm occurred in the north of Italy, resulting in a huge amount of rainfall that was recorded. The focus here is given to Cortina d’Ampezzo area in the Veneto Region, due to the high importance of the area from touristic and sport perspectives and the presence of landslides. This study is devoted to investigating the impact of the severe sudden rainfall caused by this extreme atmospheric disturbance on the trends of ground deformations over this area, adopting a risk assessment perspective. The ground deformation Time Series (TS) have been derived from the European Ground Motion Service (EGMS) Ortho products derived from Sentinel-1 InSAR data. Then, according to the daily precipitation records and considering the time of this event as a turning temporal point, the TSs of all data points have been divided into pre-event and post-event phases. After obtaining the displacement rates in each phase, the quantified magnitude of changes in the pre-post rates has been calculated through a proposed simple formulation. The spatial distribution of the deformation parameters (i.e., pre-event displacement velocity and change in the pre-post displacement velocities) is found to be a practical tool for gaining comprehensive knowledge regarding the ground deformation and the effect of the extreme weather conditions on the displacement trends over the area. Furthermore, the positioning of the occurrence of deformation parameters has been compared to the landslide inventory of the area. The results showed that the extreme rainfall caused severe acceleration of displacements, more significant in horizontal East-West direction rather than vertical Up-Down direction. Some slight deceleration has been also detected. No initiation (or reactivation) of the stable zones was witnessed, and the portions with high magnitude of pre-event velocities are the ones whose displacement rates have been remarkably modified due to the severe event.

The Impact of Climate Change and Urbanization on Compound Flood Risks in Coastal Areas: A Comprehensive Review of Methods

Many cities worldwide are increasingly threatened by compound floods resulting from the interaction of multiple flood drivers. Simultaneously, rapid urbanization in coastal areas, which increases the proportion of impervious surfaces, has made the mechanisms and simulation methods of compound flood disasters more complex. This study employs a comprehensive literature review to analyze 64 articles on compound flood risk under climate change from the Web of Science Core Collection from 2014 to 2024. The review identifies methods for quantifying the impact of climate change factors such as sea level rise, storm surges, and extreme rainfall, as well as urbanization factors like land subsidence, impervious surfaces, and drainage systems on compound floods. Four commonly used quantitative methods for studying compound floods are discussed: statistical models, numerical models, machine learning models, and coupled models. Due to the complex structure and high computational demand of three-dimensional joint probability statistical models, along with the increasing number of flood drivers complicating the grid interfaces and frameworks for coupling different numerical models, most current research focuses on the superposition of two disaster-causing factors. The joint impact of three or more climate change-driving factors on compound flood disasters is emerging as a significant future research trend. Furthermore, urbanization factors are often overlooked in compound flood studies and should be considered when establishing models. Future research should focus on exploring coupled numerical models, statistical models, and machine learning models to better simulate, predict, and understand the mechanisms, evolution processes, and disaster ranges of compound floods under climate change.