Extreme Rainfall Events Research Articles

Coupled model intercomparison project phase 6 (CMIP6) high resolution model intercomparison project (HighResMIP) bias in extreme rainfall drives underestimation of amazonian precipitation

Extreme rainfall events drive the amount and spatial distribution of rainfall in the Amazon and are a key driver of forest dynamics across the basin. This study investigates how the 3-hourly predictions in the High Resolution Model Intercomparison Project (HighResMIP, a component of the recent Coupled Model Intercomparison Project, CMIP6) represent extreme rainfall events at annual, seasonal, and sub-daily time scales. TRMM 3B42 (Tropical Rainfall Measuring Mission) 3 h data were used as observations. Our results showed that eleven out of seventeen HighResMIP models showed the observed association between rainfall and number of extreme events at the annual and seasonal scales. Two models captured the spatial pattern of number of extreme events at the seasonal and annual scales better (higher correlation) than the other models. None of the models captured the sub-daily timing of extreme rainfall, though some reproduced daily totals. Our results suggest that higher model resolution is a crucial factor for capturing extreme rainfall events in the Amazon, but it might not be the sole factor. Improving the representation of Amazon extreme rainfall events in HighResMIP models can help reduce model rainfall biases and uncertainties and enable more reliable assessments of the water cycle and forest dynamics in the Amazon.

Beyond Hydrology: Exploring the Factors Influencing the Seasonal Variation of the Molecular Composition of Riverine Dissolved Organic Matter

AbstractThe export of dissolved organic matter (DOM) from rivers is essential for linking terrestrial and marine carbon reservoirs in the global carbon cycle. However, there is limited knowledge regarding how the molecular composition of riverine DOM changes under different hydrological conditions, especially during extreme rainfall events. Moreover, the factors beyond hydrology that impact DOM composition have not been well defined. To address these gaps, samples were collected from a human‐impacted medium‐sized subtropical monsoonal river across various hydrological conditions throughout a complete hydrological cycle. Utilizing high‐resolution mass spectrometry, it was discovered that the solid‐phase extractable DOM (SPE‐DOM) during the high‐flow (1 < runoff (Q): annual mean runoff (Qm) < 3) and extreme‐rain (Q:Qm > 3) periods exhibited a higher number of molecular formulae, lower H/C, higher O/C, and a higher proportion of carboxylic‐rich alicyclic molecules compared to the low‐flow period (LFP) (Q:Qm < 1). These alterations were attributed to input from more diverse sources, particularly a greater input from soil organic matter with higher oxidation degrees. Additionally, the P‐containing formulae were more enriched during the extreme‐rain period, likely from agricultural lands and sediment release. Conversely, the fraction of S‐containing formulae was significantly higher during the LFP, possibly due to the amplified influence of anthropogenic input. Furthermore, the DOM aromaticity did not fluctuate with runoff but was significantly associated with temperature. In summary, the study indicated that the composition of DOM varied significantly under different hydrological conditions, with temperature and anthropogenic activities identified as crucial factors influencing riverine DOM export.

A Comprehensive Evaluation of Mean and Extreme Climate for the Conformal Cubic Atmospheric Model (CCAM)

Abstract This study evaluates the performance of the Conformal Cubic Atmospheric Model (CCAM) in dynamically downscaling fifth major global reanalysis produced by European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5) reanalysis data from 1985 to 2014, following a 5-yr spinup period. It focuses on daily maximum and minimum temperatures and daily precipitation, comparing CCAM to ERA5 and the Australian Gridded Climate Data (AGCD). The CCAM effectively reduces warm biases in daily minimum temperatures but struggles with cold biases in daily maximum temperatures, particularly in northern Australia during the wet season, possibly due to high-level cloud overestimation. Precipitation tends to be overestimated, especially in extreme rainfall events, though offset by an underestimation of low rainfall. The study showcases improvements in the annual minimum of daily minimum temperatures across most of Australia, while identifying challenges in forecasting cooler extreme temperatures. It adds value to annual maximum daily maximum temperatures in southern Australia but less so in the north. The analysis of the 5% annual exceedance probability (AEP5%) yields mixed results influenced by location and potential ocean temperature changes. Some coastal areas exhibit lost value, possibly linked to ocean temperature shifts. Furthermore, CCAM’s representation of maximum annual daily and 5-day rainfall reveals lost value, particularly in eastern Australia due to an overestimate of extreme rainfall. Despite the challenges of comparing a dynamical downscaling model like CCAM to ERA5, this study highlights its benefits in reducing biases, especially in temperature representation. Given the larger biases in phase 6 of Coupled Model Intercomparison Project (CMIP6) global climate models, CCAM appears suitable for dynamic downscaling in climate projections, emphasizing the need for ongoing model enhancements, including addressing biases related to ephemeral water bodies and extreme rainfall. Significance Statement This study critically assesses the performance of the Conformal Cubic Atmospheric Model (CCAM) in dynamically downscaling ERA5 reanalysis data from 1985 to 2014, offering valuable insights into climate modeling. Focusing on temperature and precipitation, CCAM proves effective in mitigating warm biases in daily minimum temperatures but encounters challenges with cold biases in daily maximum temperatures, particularly in northern Australia. The analysis reveals the overestimation of precipitation, especially in extreme events, yet identifies improvements in annual minimum daily minimum temperatures across Australia. The study underscores CCAM’s potential in reducing biases compared to CMIP6 global climate models, making it a promising tool for dynamic downscaling in climate projections. It emphasizes the necessity for ongoing model enhancements, particularly addressing biases related to ephemeral water bodies and extreme rainfall.

Identification of spatio‐temporal patterns in extreme rainfall events in the Tropical Andes: A clustering analysis approach

Identification of spatio‐temporal patterns in extreme rainfall events in the Tropical Andes: A clustering analysis approach

Impacts of climate and land-use change on flood events with different return periods in a mountainous watershed of North China

Study regionLuanhe River Basin in North China Study focusGlobal warming exacerbates hydrological cycles, leading to increased intensity and frequency of floods. Regional studies on the impacts of climate change and land cover changes on flood events in recent decades are particularly important for flood prevention and risk assessment. In this study, the Peak Over Threshold- Generalized Pareto Distribution (POT-GPD) method is used to fit extreme runoff values in the Luanhe River Basin from 1961 to 2018 to identify floods with different return periods. Six flood behavior indicators are adopted to analyze the flood characteristics and extreme climate indices are also used for breakpoint testing and trend analysis. Various scenarios are established as the input of the SWAT model to study the impacts of climate change and land-use change on the flood events. New hydrological insights for the regionThe results indicate that the 2-year and 20-year return period floods are primarily rapid-rise single-peak floods, dominated by short-duration intense precipitation. The 5-year and 10-year return period floods are mainly multi-peak floods, primarily caused by long-duration precipitation. Floods in the Luanhe River Basin are primarily influenced by climate change, however, flood events do not directly correspond to the return periods of precipitation events. Forest is the most effective land-use type for flood retention. It could reduce flood magnitudes, especially for the 20-year return period floods. However, increasing forest cover does not effectively reduce the occurrence of the 5-year or 10-year floods, i.e. multi-peak floods. With more extreme rainfall events triggered by future climate change, particular attention should be paid to long-duration precipitation events, which may result in more severe disasters and economic losses.

Multi-fidelity deep neural network with Monte Carlo dropout technique for uncertainty-aware risk recognition of backward erosion piping in dikes

Backward erosion piping (BEP) is an increasingly critical failure mechanism in dike systems, often triggered by floods resulting from extreme rainfall events, which are exacerbated by the ongoing shifts in climate patterns. This study embarks on a nuanced examination of BEP, a phenomenon that substantially undermines flood control and emergency management endeavors. Addressing the complexities of BEP incidents and the consequential variable impact on dike infrastructure, we have incorporated Monte Carlo dropout techniques within a multi-fidelity deep learning framework to enhance predictive accuracy and provide an uncertainty-aware assessment of BEP risk. Drawing on 16 sets of physical model experiments emulating dike structures from the lower Yangtze River basin in China, we conducted a detailed study of the BEP evolution mechanism under various hydrological scenarios intensified by climate change. These experiments allowed us to observe the initiation and progression of BEP in different conditions. The development of a structural equation model quantifies the effects of several critical factors—including those exacerbated by climatic variability—on BEP dynamics. Seven key factors were identified as influential to BEP risk levels, integrating distinct BEP traits, hydrological attributes, and dike engineering conditions. A Monte Carlo dropout-enhanced multi-fidelity deep neural network (MFDNN) was crafted, synthesizing low-fidelity experimental data with high-fidelity field case studies, to construct an advanced model for BEP risk level identification. Compared against four sophisticated machine learning models, our MFDNN demonstrated superior performance, effectively blending experimental insights with real-world occurrences. The proposed model emerges as a scientifically robust and pragmatic tool for delineating BEP risk levels in dike systems, providing vital guidance for categorizing BEP incidents and forging targeted, climate-informed emergency response strategies.

Probabilistic Cascade Modeling for Enhanced Flood and Landslide Hazard Assessment: Integrating Multi-Model Approaches in the La Liboriana River Basin

Extreme rainfall events in Andean basins frequently trigger landslides, obstructing river channels and causing flash flows, loss of lives, and economic damage. This study focused on improving the modeling of these events to enhance risk management, specifically in the La Liboriana basin in Salgar (Colombia). A cascading modeling methodology was developed, integrating the spatially distributed rainfall intensities, hazard zoning with the SLIDE model, propagation modeling with RAMMS using calibrated soil rheological parameters, the distributed hydrological model TETIS, and flood mapping with IBER. Return periods of 2.33, 5, 10, 25, 50, and 100 years were defined and applied throughout the methodology. A specific extreme event (18 May 2015) was modeled for calibration and comparison. The spatial rainfall intensities indicated maximum concentrations in the northwestern upper basin and southeastern lower basin. Six landslide hazard maps were generated, predicting landslide-prone areas with a slightly above random prediction rate for the 2015 event. The RAMMS debris flow modeling involved 30 simulations, indicating significant deposition within the river channel and modifying the terrain. Hydraulic modeling with the IBER model revealed water heights ranging from 0.23 to 7 m and velocities from 0.34 m/s to 6.98 m/s, with urban areas showing higher values, indicating increased erosion and infrastructure damage potential.

Exploring the driving factors of urban flood at the catchment Scale: A case study of multitype megacities in China

With the increasing frequency of extreme rainfall events worldwide, urban flood in densely populated and economically vibrant urban areas has become a pressing issue drawing global attention. Catchments are fundamental units for understanding urban hydrological processes and managing flood. Current research often lacks an investigation into the importance and spatial differences of driving factors for urban flood at the catchment scale, particularly for different types of cities. This study uses 15 megacities in China as case studies and establishes a comprehensive system of driving factors. It categorizes cities based on population levels, climate zones, and spatial locations. The study begins with hotspot analysis to identify areas with high occurrences of urban inundation. Subsequently, it employs Geographic Weighted Regression (GWR), a spatial analysis technique, to unveil the mechanisms driving the spatial distribution of inundation points in different types of cities and the spatial heterogeneity of driving factors at the urban catchment scale. The research findings, based on multiple cities, demonstrate that urban inundation points exhibit clear spatial clustering characteristics. While impervious surfaces and urban buildings are generally more important than factors such as landscape patterns and population density, the importance and explanatory power of driving factors for the spatial distribution of urban inundation vary across different cities and even within different catchments of the same city. The categorization of cities based on population levels and spatial locations demonstrates stronger regularities in driving mechanisms compared to categorization based on climate zones. The study’s results further underscore the complex driving mechanisms behind the spatial distribution of urban inundation. This highlights the need for flood management strategies that consider both universal patterns of inundation occurrence and the specific characteristics of local driving factors to develop targeted and effective mitigation measures.

Simulating springtime extreme rainfall over Southern East Asia: unveiling the importance of synoptic-scale activities

This study investigates the influence of synoptic-scale activities on extreme precipitation during March–April–May (MAM) over Southern East Asia (SEA) using observational data and compares the results with the outputs from 20 Coupled Model Intercomparison Project phase 6 (CMIP6) historical runs. Observations show that SEA intense daily precipitation in MAM is linked to enhanced upper-level synoptic-scale waves; these disturbances are associated with significant anomalous temperature advection as well as moisture flux convergence, creating favorable conditions for extreme rainfall. Furthermore, it is found that a temperature advection index (TAI) can be utilized to characterize such synoptic-scale activities. Inspection of CMIP6 historical runs reveals that, among 20 models, 13 models perform well in accurately capturing the observed SEA rainfall pattern; such extreme events are also closely linked to TAI in the model environment. Overall, observed (simulated) results show that 78% (75%) of extreme events in the Yangtze River Basin–South Korea–south of Japan region can be attributed to positive TAI. Additionally, the related circulation anomalies such as the upper-level synoptic-scale wave feature, temperature advection, and moisture anomalies from these models closely resemble those observed during extreme precipitation days in SEA. Our findings suggest that TAI can effectively indicate both the frequency and intensity of extreme rainfall events in SEA, along with the associated synoptic-scale activities. Further study reveals a close lead-lag correlation between TAI and rainfall patterns over SEA. This correlation is characterized by eastward-propagating wave trains across the entire troposphere. Consequently, TAI not only acts as a benchmark for quantifying synoptic-scale extreme rainfall in SEA but also shows potential in predicting SEA rainfall linked to synoptic-scale disturbances.

Runoff Control Performance of Three Typical Low-Impact Development Facilities: A Case Study of a Community in Beijing

The development of sponge cities advocates for sustainable urban rainwater management, effectively alleviating urban flood disasters, reducing non-point-source pollution, and promoting the recycling of rainwater resources. Low-Impact Development (LID) serves as a key strategy in this context, providing essential support for urban rainwater control and pollution reduction. To investigate the runoff control effects of LID measures and to reveal the relationship between facility runoff control performance and installation scale, this study focuses on a sponge community in Beijing. A SWMM model was constructed to analyze the rainwater flood control and pollutant load reduction effects of different LID facilities, including bio-retention cells, green roofs, and permeable pavements. Using evaluation indicators such as surface runoff, node overflow, and pollutant control rates, this study examined how facility performance varies with installation scale under different rainfall conditions. The combination scheme of LID equipment optimal configuration is designed by using multiple criteria decision analysis (MCDA) and cost–benefit theory. The results indicate significant differences in performance among the various LID facilities across different rainfall scenarios. Specifically, the optimal installation proportion for runoff and overflow control of permeable pavements were found to be between 30% and 70%. Green roofs demonstrate superior performance in handling extreme rainfall events, while bio-retention cells exhibit significant effectiveness in controlling Total Suspended Solids (TSSs). Through comprehensive performance evaluation, this study identified the optimal combination scale under a 3-year rainfall recurrence interval as 30% permeable pavements, 20% green roof, and 60% bio-retention cells. This combination effectively leverages the strengths of each facility, ensuring system stability and efficiency while also demonstrating optimal management efficiency in cost–benefit analyses. The findings of this research provide valuable insights for future urban water management and infrastructure development.

Seasonal, Decadal, and El Niño-Southern Oscillation-Related Trends and Anomalies in Rainfall and Dry Spells during the Agricultural Season in Central Malawi

As governments continue to address climate change when formulating policy, there remains a need to determine if such a change exists in the historical record to inform clear indices for monitoring the present climate for site-specific interventions. This study characterised trends and anomalies in rainfall and dry spells, providing local information often projected from satellites or regional data in data-scarce regions. From 1961 to 2007, daily rainfall records in Central Malawi were used to calculate indices for low-(Balaka), medium-(Bunda, Chitedze, KIA), and high-altitude (Dedza) sites, which were then subjected to Mann–Kendall’s, Cramer’s, and Spearman-Rho’s trend tests. Significant decreasing trends in terms of wet days and growing season length were evident across locations. Seasonal and extreme rainfall, dry spells, and inter-seasonal and near-decadal anomalies were not consistently or inevitably significant. Unexpectedly, rainfall anomalies were largest in Bunda and KIA, which have mild climatic regimes, while the lowest were in Balaka, a rainfall-averse zone. The relationship between El Niño-Southern Oscillation (ENSO) and extreme rainfall and dry spell events did not reach statistical significance. In conclusion, extreme precipitation and dry spell events show varied intensities and proportions rather than increased frequency. The disparate results largely justify the need for in-depth local-scale assessments for agroclimatic applications.

An Analysis of Extreme Rainfall Events in Cambodia

An Analysis of Extreme Rainfall Events in Cambodia

Influence of galactic cosmic ray flux on extreme rainfall events in Greece and Libya

The Galactic Cosmic Rays (GCR) flux can contribute to the formation of condensation nuclei (CN), radionuclides, and other particles, which in turn influence the formation of rain and extreme weather events. The aim of this analysis was to investigate the possible influence of GCR flux on the extreme rainfall events that occurred in Greece and Libya in September 2023. We used time series data for GCR, rainfall estimates from ERA5, and Sea Surface Temperature (SST) for the period between September 1, 2023, and September 11, 2023. The results revealed a negative correlation between GCR and SST of −0.807 (Greece) and −0.828 (Libya), and a positive correlation between precipitation and SST of +0.972 (Greece) and +0.998 (Libya). The GCR flux and SST accounted for approximately 60.52% and 34.53% of the extreme event in Greece, and 33.71% and 65.96% in Libya, respectively. These statistical results indicate that GCR flux contributed to the formation of the extreme precipitation event that caused significant destruction in Greece and Libya in September 2023.

Forecasting road network functionality states during extreme rainfall events to facilitate real-time emergency response planning

Rapid prediction of Road Network Functionality (RNF) during extreme rainfall-induced flooding is crucial for supporting proactive and real-time emergency planning, such as rescue, evacuation planning, and emergency supply distribution. Unlike normal operational conditions, extreme rainfall events introduce complex non-stationary, non-Euclidean characteristics to RNF due to intricate meteorological and hydrological processes, as well as the role of a community's road network in emergency response planning. Conventional physics-based flood simulations and flow-based road network analyses typically lack the computational efficiency required for real-time RNF predictions, hindering timely risk mitigation decisions. This study leverages the accuracy of physics-based simulations and the efficacy of deep-learning technologies to develop a deep learning-based surrogate model for Rain-to-RNF (R2R) predictions. This model couples Long Short-Term Memory (LSTM) networks with Spatial-Temporal Graph Convolutional Networks (ST-GCNs) to uniquely capture the spatiotemporal dynamics of RNF under extreme rainfall events. The predictive accuracy, stability, and versatility of the R2R surrogate model are demonstrated in four flood-prone communities in Zhejiang Province. Its implementation during Typhoon Fitow (2013) over a 30-hour intense rainfall showcases its promising predictive capacity and unparalleled computational efficiency. This research advances disaster management, enhancing the resilience and responsiveness of community infrastructure during extreme weather events.

Impact of tropical waves on extreme rainfall events during coastal El Niño

Abstract We examine the relationship between convectively coupled waves, the Madden–Julian Oscillation (MJO), and extreme precipitation over the western coast of South America during Coastal El Niño (COEN) events for the period spanning 1980–2023. Two types of COEN can be distinguished: (i) that occur in association with large-scale El Niño Southern Oscillation (ENSO) (e.g. 1982/83, 1997/98), and (ii) more ‘local’ COEN–when anomalous sea surface temperature take place over the far-eastern equatorial Pacific only (e.g. 2017, 2023). During both types of COEN events, increased rainfall along the western coast of South America is associated with intense Kelvin wave activity. In addition, westward inertio-gravity (WIG) waves, Rossby waves, and the MJO exhibit increased activity during local COEN events. During the recent extreme COEN 2017 and 2023, heavy rainfall occurred alongside significant WIGs, Kelvin, Rossby, and MJO events with unprecedented amplitudes propagating along western South America. Our results suggest that the probability of extreme precipitation under Coastal ENSO in western South America is strongly modulated by wave activity.

Response of hydrology and nutrient losses to different extreme rainfall conditions in a coastal watershed influenced by orchards

Global warming is altering the frequency of extreme rainfall events and introducing uncertainties for non-point source pollution (NPSP). This research centers on orchard-influenced planting areas (OIPA) in the Wulong River Watershed of Shandong Province, China, which are known for their heightened nitrogen (N) and phosphorus (P) pollution. Leveraging meteorological data from both historical (1989–2018) and projected future periods (2041–2100), this research identified five extreme rainfall indices (ERI): R10 (moderate rain), R20 (heavy rain), R50 (rainstorm), R95p (Daily rainfall between the 95th and 99th percentile of the rainfall), and R99p (>99th percentile). Utilizing an advanced watershed hydrological model, SWAT-CO2, this study carried out a comparison between ERI and average conditions and evaluated the effects of ERI on the hydrology and nutrient losses in this coastal watershed. The findings revealed that the growth multiples of precipitation in the OIPA for five ERI varied between 16 and 59 times for the historical period and 14 to 65 times for future climate scenarios compared to the average conditions. The most pronounced increases in surface runoff and total phosphorus (TP) loss were observed with R50, R95p, and R99p, showing growth multiples as high as 352 and 330 times, and total nitrogen (TN) growth multiples varied between 4.6 and 30.3 times. The contribution rates of R50 and R99p for surface runoff and TP loss in the OIPA during all periods exceeded 55%, however, TN exhibited the opposite trend, primarily due to the dominated NO3-N leaching in the sandy soil. This research revealed how the OIPA reacts to different ERI and pinpointed essential elements influencing water and nutrient losses.

Projected future changes in food insecurity hotspots over the IGAD region of Eastern Africa

Food insecurity is a major issue in many parts of the world, driven by conflict, economic instability, environmental challenges, and poor governance processes. Understanding the impact of future rainfall extremes on areas already experiencing food insecurity is crucial. This study investigates how food insecurity hotspots (FIH), food crisis frequency, and duration will change in the near future (2021–2050) and far future (2071–2100) under Shared Socioeconomic Pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The study utilizes precipitation data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and FIH data from the NASA Socioeconomic Data and Applications Center (SEDAC). To calculate future exposure and vulnerability to FIH, as well as food crisis frequency and duration, weighted sum models were used. The results indicate that arid and semi-arid areas in northeastern Kenya, most of Somalia, zones in southeastern Ethiopia, most of Djibouti, and central and northern Sudan are highly vulnerable to future extreme rainfall events, an increase in FIH cases, and longer food crisis frequency and duration in the near future (2021–2050) and far future (2071–2100) under all scenarios. On the other hand, most districts in Uganda, southern and southwestern South Sudan, counties in western Kenya, and the majority of zones in western Ethiopia are projected to have very few FIH cases, low food crisis frequency, and duration in both the near and far future under all scenarios. These findings are crucial for early warning systems, humanitarian responses, and food security interventions. We recommend harnessing projected increases in rainfall for water harvesting in Kenya, as well as promoting cash and food crop production in central and western Ethiopia, central and northern Uganda, and most of South Sudan.

Extreme Rainfall Events Triggered Loess Collapses and Landslides in Chencang District, Shanxi, China, during June–October 2021

Extreme Rainfall Events Triggered Loess Collapses and Landslides in Chencang District, Shanxi, China, during June–October 2021

Investigation the Impact of the Climate Change on Intensity Duration Frequency (IDF) Curve Development: Case Study at Hulu Terengganu, Malaysia

The intensity-duration-frequency (IDF) curves are the most common form of design rainfall data used for peak discharge estimation. Thus, the IDF curve needs to be improved with the expectation that rainfall intensity and frequency have increased as a result of climate change. The main purpose of this study was to investigate the changes of IDF curves considering the climate change impacts on Hulu Terengganu. The climate projection from MRI-ESM2-0, CMCC-CM2-SR5, and GFDL-ESM4 under 3 different scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were used to provide the climate changes pattern in the future year (∆2050). In order to downscale the climate projection, the statistical downscaling method (SD-LS) was employed to correct the biases of these three GCMs. The IDF curves for the return periods of 2, 5, 10, 20, 50, 100, and 200 year were then developed based on the maximum rainfall intensity that were projected by the SD-LS model. The results clearly indicated that there are possibilities for increasing patterns in the projected annual and monthly rainfall for both time periods compared to historical data. Thus, the future extreme rainfall events for various durations with different return periods are all likely to increase over time. The largest potential increase is predicted at Sg. Gawi (+2.0% to +86.0%) based on the different return periods and rainfall durations. It could change the pattern of IDF curve that been developed based on projected rainfall by various SSPs. The developed IDF curves shows higher rainfall intensities in a shorter duration under the same return periods. Therefore, comprehensive action must be taken immediately to regulate and manage the effects of climate change.

Tiny rain makers: How aerosols shape extreme rainfall simulation accuracy

Given the rapid spatiotemporal variability of aerosols and their complex impacts on the Earth system, investigating the role of aerosols in modelling extreme weather events, particularly various extreme rainfalls, remains limited. This study explored extreme rainfall simulation sensitivity to aerosol properties, transport and seasonality over the UK and Ireland during four seasons in 2020 by the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model. Two sets of high-resolution simulations, one including aerosol direct and indirect effects and the other one without any aerosol effects, were conducted to investigate the improvements due to aerosol inputs. Meteorological results were verified using ground and satellite observations to examine the reliability of simulations. The 12 extreme rainfall events during the study months were classified by their backward trajectories from the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, and their simulation accuracies were quantified through six spatiotemporal metrics and one overall score. By comprehensive analyses of the simulation performances under different conditions, it is found that the model performs better in simulating rainfall events driven by sea salt aerosols (SSA) compared to those promoted by anthropogenic aerosols. Air masses transported from the Arctic and North Atlantic Ocean also help achieve higher rainfall simulation accuracy than European air masses. Rainfall simulations for winter and autumn events outperform those for spring and summer events. When considering aerosol effects in simulations, almost half of the events showed improved performance, while an equal number experienced a decrease and a small proportion remained unchanged. The most remarkable improvements are observed in rainfall simulations with higher concentrations of SSA and the participation of anthropogenic aerosols, while simulations promoted only by anthropogenic aerosols all experience performance reductions. The aerosol effects also led to significant increases in monthly rainfalls, accompanied by great reductions of Cl in larger particle size aerosols and NO3 in smaller particle size aerosols.