High-resolution Regional Climate Model Research Articles

The added value of using convective-permitting regional climate model simulations to represent cloud band events over South America

AbstractClimate science has long explored whether higher resolution regional climate models (RCMs) provide improved simulation of regional climates over global climate models (GCMs). The advent of convective-permitting RCMs (CPRCMs), where sufficiently fine-scale grids allow explicitly resolving rather than parametrising convection, has created a clear distinction between RCM and GCM formulations. This study investigates the simulation of tropical-extratropical (TE) cloud bands in a suite of pan-South America convective-permitting Met Office Unified Model (UM) and Weather Research and Forecasting (WRF) climate simulations. All simulations produce annual cycles in TE cloud band frequency within 10–30% of observed climatology. However, too few cloud band days are simulated during the early summer (Nov–Dec) and too many during the core summer (Jan–Feb). Compared with their parent forcing, CPRCMs simulate more dry days but systematically higher daily rainfall rates, keeping the total rain biases low. During cloud band systems, the CPRCMs correctly reproduced the observed changes in tropical rain rates and their importance to climatology. Circulation analysis suggests that simulated lower subtropical rain rates during cloud bands systems, in contrast to the higher rates in the tropics, are associated with weaker northwesterly moisture flux from the Amazon towards southeast South America, more evident in the CPRCMs. Taken together, the results suggest that CPRCMs tend to be more effective at producing heavy daily rainfall rates than parametrised simulations for a given level of near-surface moist energy. The extent to which this improves or degrades biases present in the parent simulations is strongly region-dependent.

Evaluation of Wind Speed Accuracy Enhancement in South Asia Through Terrain-Modified Wind Speed (Wt) Adjustments of High-Resolution Regional Climate Modeling

Evaluation of Wind Speed Accuracy Enhancement in South Asia Through Terrain-Modified Wind Speed (Wt) Adjustments of High-Resolution Regional Climate Modeling

Projected change in the rainfall behaviour over Odisha

ABSTRACT Climate change has impacted the rainfall characteristics and the extremes are on the rise across the world. These changes threaten agriculture, water resources, and disaster management. Odisha, an eastern Indian state with an agrarian economy, heavily relies on monsoon rainfall. The study analyses the projected change in the rainfall characteristics across Odisha at different specific warming targets of the Paris Climate Agreement using high-resolution regional climate models. The study adopts a bi-fold approach; first, it employs a robust method to select the best model experiments; afterwards, the model ensemble is used to examine the projected rainfall characteristics. The results indicate a 4–16% increase in projected rainfall over Odisha, with an extended rainy season. The projected number of consecutive wet days, moderate and extreme rainfall, is expected to rise under the global warming scenario. The prolonged rainy season with heavy rainfall can result in disasters like post-monsoon floods, while higher rainfall variability will increase the risk of floods and droughts across Odisha, threatening agriculture. The results will help pinpoint regions most vulnerable to climate change. The study also suggested measures to assist governments and planners in developing short-term and long-term strategies for adaptation and mitigation to lessen climate change impacts.

On the suitability of a convolutional neural network based RCM-emulator for fine spatio-temporal precipitation

High resolution regional climate models (RCM) are necessary to capture local precipitation but are too expensive to fully explore the uncertainties associated with future projections. To resolve the large cost of RCMs, Doury et al. (2023) proposed a neural network based RCM-emulator for the near-surface temperature, at a daily and 12 km-resolution. It uses existing RCM simulations to learn the relationship between low-resolution predictors and high resolution surface variables. When trained the emulator can be applied to any low resolution simulation to produce ensembles of high resolution emulated simulations. This study assesses the suitability of applying the RCM-emulator for precipitation thanks to a novel asymmetric loss function to reproduce the entire precipitation distribution over any grid point. Under a perfect conditions framework, the resulting emulator shows striking ability to reproduce the RCM original series with an excellent spatio-temporal correlation. In particular, a very good behaviour is obtained for the two tails of the distribution, measured by the number of dry days and the 99th quantile. Moreover, it creates consistent precipitation objects even if the highest frequency details are missed. The emulator quality holds for all simulations of the same RCM, with any driving GCM, ensuring transferability of the tool to GCMs never downscaled by the RCM. A first showcase of downscaling GCM simulations showed that the RCM-emulator brings significant added-value with respect to the GCM as it produces the correct high resolution spatial structure and heavy precipitation intensity. Nevertheless, further work is needed to establish a relevant evaluation framework for GCM applications.

Future climate projection across Tanzania under CMIP6 with high-resolution regional climate model

Climate change is one of the most pressing challenges faced by developing countries due to their lower adaptive capacity, with far-reaching impacts on agriculture. The mid-century period is widely regarded as a critical moment, during which adaptation is deemed essential to mitigating the associated impacts. This study presents future climate projections across Tanzania using the latest generation of global climate models (CMIP6) combined with a high-resolution regional climate model. The findings indicate that, the trends in temperature and precipitation in Tanzania from 1991 to 2020, minimum temperatures showed the highest variability with a trend of 0.3 °C, indicating significant fluctuations in minimum temperature over the decades. Maximum temperatures also showed high variability with a trend of 0.4 °C. There is a range of variability in precipitation per decade for different regions in Tanzania, with some regions experiencing significant decreases in precipitation of up to − 90.3 mm and − 127.6 mm. However, there were also regions that experienced increases in precipitation, although these increases were generally less than 4.8 mm over the decades. The projections of minimum and maximum temperatures from 2040 to 2071 under the Shared Socioeconomic Pathways (SSP) 2–4.5 and SSP 5–8.5 are projected to increase by 0.14 °C to 0.21 °C per decade, across different regions. The average projected precipitation changes per decade vary across regions. Some regions are projected to experience increases in precipitation. Other regions are projected to show decreases in precipitation within the range of − 0.6 mm to 15.5 mm and − 1.5 mm to 47.4 mm under SSP2–4.5 and SSP5–8.5 respectively. Overall, both scenarios show an increase in projected temperatures and precipitation for most regions in Tanzania, with some areas experiencing more significant increases compared to others. The changes in temperatures and precipitation are expected to have significant impacts on agriculture and water resources in Tanzania.

Evaluation and Projection of Population Exposure to Temperature Extremes over the Beijing–Tianjin–Hebei Region Using a High-Resolution Regional Climate Model RegCM4 Ensemble

Evaluation and Projection of Population Exposure to Temperature Extremes over the Beijing–Tianjin–Hebei Region Using a High-Resolution Regional Climate Model RegCM4 Ensemble

Future Projections of Precipitation Extremes for Greece Based on an Ensemble of High-Resolution Regional Climate Model Simulations

An assessment of the projected changes in precipitation extremes for the 21st century is presented here for Greece and its individual administrative regions. The analysis relies on an ensemble of high-resolution Regional Climate Model (RCM) simulations following various Representative Concentration Pathways (RCP2.6, RCP4.5, and RCP8.5). The simulated changes in future annual total precipitation (PRTOT) under the examined scenarios are generally negative but statistically non-robust, except towards the end of the century (2071–2100) over high-altitude mountainous regions in Western Greece, Peloponnese, and Crete under RCP8.5. The pattern of change in the number of very heavy precipitation days (R20) is linked to the respective pattern of the PRTOT change with a statistically robust decrease of up to −5 days per year only over parts of the high-altitude mountainous regions in Western Greece, Peloponnese, and Crete for 2071–2100 under RCP8.5. Contrasting the future tendency for decrease in total precipitation and R20, the changes in the intensity of precipitation extremes show a tendency for intensification. However, these change patterns are non-robust for all periods and scenarios. Statistical significance is indicated for the highest 1-day precipitation amount in a year (Rx1day) for the administrative regions of Thessaly, Central Greece, Ionian Islands, and North Aegean under RCP8.5 in 2071–2100. The changes in the contribution of the wettest day per year to the annual total precipitation (RxTratio) are mainly positive but non-robust for most of Greece and all scenarios in the period 2021–2050, becoming more positive and robust in 2071–2100 for RCP8.5. This work highlights the necessity of taking into consideration high-resolution multi-model RCM estimates in future precipitation extremes with various scenarios, for assessing their potential impact on flood episodes and the strategic planning of structure resilience at national and regional level under the anticipated human-induced future climate change.

On the Sensitivity of the Potential Evapotranspiration of Egypt to Different Initial Conditions of the Soil Moisture Using a High-resolution Regional Climate Model

The present study investigated the sensitivity of the Potential Evapotranspiration (PET) of Egypt to different initial conditions of soil moisture using a high-resolution Regional Climate Model (RegCM4). To address this issue, two 32-year simulations were conducted from 1979 to 2010. The first two years were considered as spin-up. In the first simulation, the RegCM4 was initialized with the satellite product ESACCI (ESA) and the RegCM4 was initialized with version 3 of the Century Reanalysis Product (CEN) in the second simulation. The PET was computed using a calibrated version of the Hargreaves–Samani (HS) equation. In the two simulations, the RegCM4 was configured with the Community Land Model version 4.5 (CLM45). Era-Interim Reanalysis of 1.5 degrees (EIN15) was used to downscale the RegCM4 with 25 km grid spacing. The ERA5-based product (ERA5) was used as the observational dataset. The results showed that switching between ESA and CEN didn’t induce a notable influence on the daily maximum air temperature. Regarding the daily minimum air temperature, the CEN outperforms the ESA concerning the Climate Research Unit (CRU). In comparison with the ERA5 product, the CEN was able to reduce the PET negative bias (0.8 to 1.8 mm day-1) better than the one noted in the ESA (0.8 to 3 mm day-1). On a point scale, the performance of the ESA/CEN varies with the location and month. Such a point was confirmed using different statistical metrics such as mean bias, Pearson correlation coefficient and standard deviation ratio. In conclusion, the CEN can be recommended to initialize the RegCM4 to ensure a reliable estimation of the PET of Egypt either in the present climate or under different future scenarios.

Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?

Abstract. Snow dynamics play a critical role in the climate system, as they affect the water cycle, ecosystems, and society. In climate modelling, the representation of the amount and extent of snow on the land surface is crucial for simulating the mass and energy balance of the climate system. Here, we evaluate simulations of daily snow depths against 83 station observations in southern Germany in an elevation range of 150 to 1000 m over the time period 1987–2018. Two simulations stem from high-resolution regional climate models – the Weather Research & Forecasting (WRF) model at 1.5 km resolution and the COnsortium for Small scale MOdelling model in CLimate Mode (COSMO-CLM; abbreviated to CCLM hereafter) at 3 km resolution. Additionally, the hydrometeorological snow model Alpine MUltiscale Numerical Distributed Simulation ENgine (AMUNDSEN) is run at point scale at the locations of the climate stations, based on the atmospheric output of CCLM. To complement the comparison, the ERA5-Land dataset (9 km), a state-of-the-art reanalysis land-surface product, is also compared. All four simulations are driven by the atmospheric boundary conditions of ERA5. Due to an overestimation of the snow albedo, the WRF simulation features a cold bias of 1.2 °C, leading to the slight overestimation of the snow depth in low-lying areas, whereas the snow depth is underestimated at snow-rich stations. The number of snow days (days with a snow depth above 1 cm) is reproduced well. The WRF simulation can recreate extreme snow depths, i.e. annual maxima of the snow depth, their timings, and inter-station differences, and thereby shows the best performance of all models. The CCLM reproduces the climatic conditions with very low bias and error metrics. However, all snow-related assessments show a strong systematic underestimation, which we relate to deficiencies in the snow module of the land-surface model. When driving AMUNDSEN with the atmospheric output of the CCLM, the results show a slight tendency to overestimate snow depth and number of snow days, especially in the northern parts of the study area. Snow depth extremes are reproduced well. For ERA5-Land (ERA5L), the coarser spatial resolution leads to larger differences between the model elevation and the station elevation, which contributes to a significant correlation of climatic biases with the elevation bias. In addition, the mean snow depth and number of snow days are strongly overestimated, with conditions that are too snowy in the late winter. Extreme snow depth conditions are reproduced well in the low-lying areas, whereas strong deviations occur with more complex topography. In sum, due to the high spatial resolution of convection-permitting climate models, they show the potential to reproduce the winter climate (temperature and precipitation) in southern Germany. However, different sources of uncertainties, i.e. the spatial resolution, the snow albedo parametrisation, and other parametrisations within the snow model, prevent their further use in a straightforward manner for impact research. Hence, careful evaluation is needed before any impact-related interpretation of the simulations, such as in the context of climate change research.

Uncertainty in simulated streamflow using runoff driven by the outputs of a high-resolution regional climate model

Uncertainty in simulated streamflow using runoff driven by the outputs of a high-resolution regional climate model

Possible changes and occurrences of future wave power in an enclosed sea using a high-resolution regional climate model

The present study evaluates future wave power resources and detects a possible impact of climate change on wave power in an enclosed sea (Black Sea) for the 21st century. The wave data are obtained from a third-generation spectral wave model using a validated RCM (Regional Climate Model). The wave power projection and projected occurrences are analyzed for near and middle future (NF, MF) periods based on two RCP (Representative Concentration Pathway) scenarios (4.5 and 8.5). Possible changes in wave power for the NF 2021–2060 and MF 2061−2100 periods are evaluated by comparing the historical (1970–2005) period. According to two RCP scenarios, the wave power potential for the NF is expected to increase markedly over the entire basin, while, for the MF, the changes are projected by decreases, especially in the northeastern and western parts of the basin. Furthermore, projected occurrences are examined using Energy Level Occurrences known as available, moderate, and rich level occurrences. The 15 reference points over the basin are chosen to detect the most suitable location(s) for future wave power developments. All spatial and local analyses indicate that the western basin, especially the southwest, will continue having high wave power potential and occurrences in future periods.

Climatic impacts induced by winter wheat irrigation over North China simulated by the nonhydrostatic RegCM4.7

Quantification of the impact of winter wheat irrigation on the climate and the occurrence of extreme climatic events over North China is crucial for regional adaptation planning. Previous related studies mainly focused on the impact on surface processes; however, few focused on the effects of extreme events using high-resolution nonhydrostatic regional climate models. Here, the 9-km-resolution nonhydrostatic RegCM4.7 was coupled with a crop irrigation scheme and an updated winter wheat irrigation dataset to better simulate irrigation effects. Two experiments were conducted with and without winter wheat irrigation to isolate the effects of irrigation. Results showed that irrigation simulation reduces the model biases in temperature, precipitation, latent heat flux, soil moisture, sensitive heat flux, and top-layer soil moisture. Moreover, it also reduces the bias and increases the correlation with observations obtained in irrigated areas, especially in summer, indicating better representation of irrigation schemes. Winter wheat irrigation tends to cause substantial cooling of the local surface maximum, minimum, and mean air temperatures (by −1.68, −0.34, and −0.79 °C, respectively) over irrigated areas of North China, with the largest changes observed in relation to maximum temperature. Additionally, precipitation is found to increase during spring and summer, which is strongly related to water vapor transport in the lower levels of the atmosphere. Further analyses indicated that the number of annual mean hot days decrease (−13.9 d), whereas the number of both comfort days (+10.2 d) and rainy days (days with total precipitation greater than 1 mm: +6.6 d) increase over irrigated areas, demonstrating beneficial feedback to human perception and agriculture. Fortunately, although the heat wave risk increases (number of annual mean heat wave days: +5.8 d), the impact is limited to small areas within irrigated region. Additionally, no notable change was found in terms of heavy rainfall events and precipitation intensity, which might be an undereastimation caused by the less water use in model simulation. Although winter wheat irrigation does not have notable impact on the climate of the surrounding region, it is an important factor for the local-scale climate.

Larger wind turbines as a solution to reduce environmental impacts

The EU aims for carbon neutrality by 2050, focusing on offshore wind energy. Investments in North Sea wind farms, with optimal wind resources, play a crucial role. We employed a high-resolution regional climate model, which incorporates a wind farm parametrization, to investigate and address potential mitigating impacts of large wind farms on power generation and air-sea fluxes. Specifically, we examined the effects of replacing 5 MW turbines with larger 15 MW turbines while maintaining total capacity. Our study found that substituting 15 MW turbines increases the capacity factor by 2–3%, enhancing efficiency. However, these turbines exhibit a slightly smaller impact on 10 m wind speed (1.2–1.5%) and near-surface kinetic energy (0.1–0.2%), leading to reduced effects on sea surface heat fluxes compared to 5 MW turbines. This was confirmed by a stronger reduction in net heat flux of about 0.6–1.3% in simulations with 5 MW compared to 15 MW wind turbines. Air-sea fluxes influence ocean dynamics and marine ecosystems; therefore, minimizing these impacts is crucial. Overall, deploying 15 MW turbines in offshore wind farms may offer advantages for ocean dynamics and marine ecosystems, supporting the EU's carbon–neutral objectives.

Comparison of RegCM4.7.1 Simulation with the Station Observation Data of Georgia, 1985–2008

The global climate change, driven by natural processes and increasing human activities, is especially significant for Georgia. The region is experiencing increases in temperature, desertification, redistribution of precipitation, and a rise in the frequency and severity of extreme weather events. Georgia’s complex topography and its proximity to the Black and Caspian seas make it essential to employ high-resolution regional climate models to evaluate future climate change risks. In this study, we examine the results of a high-resolution simulation of mean and extreme precipitation and temperature using the Abdus Salam International Centre for Theoretical Physics Regional Climate Model version 4.7.1 for the period 1985–2008, providing an initial evaluation of the model’s performance for the territory of Georgia. The model domain (1524 km; 2388 km) encompasses the entirety of Georgia’s territory and surrounding regions. The simulation, conducted at a 12 km horizontal grid spacing using ERA5 data as boundary conditions, indicates that the least discrepancy between observed and modeled average annual temperatures and precipitation, falling within a −1 to 1 °C and −200 to 200 mm range, respectively, was observed at most stations of eastern Georgia. The largest disparities between the model and observed average annual precipitation totals were noted along the Black Sea coast, in the Kolkheti Lowland, and in some high mountain stations in western Georgia. The most significant differences in average annual temperatures between the model and observations were observed in Ambrolauri, Mt. Sabueti, and Dedoplistskaro. For Georgia territory, such a long run with such a high resolution using ERA5 as boundary conditions was conducted for the first time. Overall, the modeling results are quite satisfactory, providing a solid basis for the successful utilization of the regional climate model RegCM4.7.1 with the selected parameterization for modeling monthly mean and extreme temperatures and precipitation in Georgia.

Improving our understanding of future tropical cyclone intensities in the Caribbean using a high-resolution regional climate model

The Caribbean region is prone to the strong winds and low air pressures of tropical cyclones and their corresponding storm surge that driving coastal flooding. To protect coastal communities from the impacts of tropical cyclones, it is important to understand how this impact of tropical cyclones might change towards the future. This study applies the storyline approach to show what tropical cyclones Maria (2017) and Dorian (2019) could look like in a 2 °C and 3.4 °C warmer future climate. These two possible future climates are simulated with a high-resolution regional climate model using the pseudo global warming approach. Using the climate response from these simulations we apply a Delta-quantile mapping technique to derive future changes in wind speed and mean sea level pressure. We apply this Delta technique to tropical cyclones Maria and Dorian’s observed wind and pressure fields to force a hydrodynamic model for simulating storm surge levels under historical and future climate conditions. Results show that the maximum storm surge heights of Maria and Dorian could increase by up to 0.31 m and 0.56 m, respectively. These results clearly show that future changes in storm surge heights are not negligible compared to end-of-the-century sea level rise projections, something that is sometimes overlooked in large-scale assessments of future coastal flood risk.

Severe reduction in spawning days and larval abundance of walleye pollock under future warming in the western East/Japan Sea

The collapse of walleye pollock catch in the Korean fishing region during the late 1980s remains unresolved despite enormous efforts to recover its stock. To investigate the future fate of walleye pollock in the western East/Japan Sea (EJS) in the late 21st century, we implemented a dynamical downscaling approach by developing high-resolution regional ocean climate models (1/20°) with the selected Coupled Model Intercomparison Project 6 (CMIP6) global climate models. We analyzed the changes in the spawning days and larval distribution of walleye pollock in the western EJS. Under the SSP5–8.5 global warming scenario, the suitable spawning period ratio of walleye pollock was drastically reduced by 76%. The severe reduction in spawning days in the western EJS was mainly attributed to ocean warming caused by a decrease in atmospheric surface cooling. Moreover, our particle tracking experiment showed substantial loss of eggsand larvae south of 38° N. Our study projected a drastic collapse of walleye pollock in the western EJS and proposed a variety of collapse patterns based on local circulation under future warming conditions. These findings can help the local fishing industry adapt and assist neighboring governments in planning future fisheries management strategies.

Cloud- and ice-albedo feedbacks drive greater Greenland Ice Sheet sensitivity to warming in CMIP6 than in CMIP5

Abstract. The Greenland Ice Sheet (GrIS) has been losing mass since the 1990s as a direct consequence of rising temperatures and has been projected to continue to lose mass at an accelerating pace throughout the 21st century, making it one of the largest contributors to future sea-level rise. The latest Coupled Model Intercomparison Project Phase 6 (CMIP6) models produce a greater Arctic amplification signal and therefore also a notably larger mass loss from the GrIS when compared to the older CMIP5 projections, despite similar forcing levels from greenhouse gas emissions. However, it is also argued that the strength of regional factors, such as melt–albedo feedbacks and cloud-related feedbacks, will partly impact future melt and sea-level rise contribution, yet little is known about the role of these regional factors in producing differences in GrIS surface melt projections between CMIP6 and CMIP5. In this study, we use high-resolution (15 km) regional climate model simulations over the GrIS performed using the Modèle Atmosphérique Régional (MAR) to physically downscale six CMIP5 Representative Concentration Pathway (RCP) 8.5 and five CMIP6 Shared Socioeconomic Pathway (SSP) 5-8.5 extreme high-emission-scenario simulations. Here, we show a greater annual mass loss from the GrIS at the end of the 21st century but also for a given temperature increase over the GrIS, when comparing CMIP6 to CMIP5. We find a greater sensitivity of Greenland surface mass loss in CMIP6 centred around summer and autumn, yet the difference in mass loss is the largest during autumn with a reduction of 27.7 ± 9.5 Gt per season for a regional warming of +6.7 ∘C and 24.6 Gt per season more mass loss than in CMIP5 RCP8.5 simulations for the same warming. Assessment of the surface energy budget and cloud-related feedbacks suggests a reduction in high clouds during summer and autumn – despite enhanced cloud optical depth during autumn – to be the main driver of the additional energy reaching the surface, subsequently leading to enhanced surface melt and mass loss in CMIP6 compared to CMIP5. Our analysis highlights that Greenland is losing more mass in CMIP6 due to two factors: (1) a (known) greater sensitivity to greenhouse gas emissions and therefore warmer temperatures and (2) previously unnotified cloud-related surface energy budget changes that enhance the GrIS sensitivity to warming.

Validation of CORDEX Regional climate models to simulate climate trends and variability across various agro-ecological zones of North Shewa, Ethiopia

Climate variability significantly affects the overall agricultural production and productivity over space and time. To quantify the effect of climate variability on natural systems on Earth in the future, the use of regional climate models (RCMs) as a tool is mandatory. Thus, the use of well-validated RCM provides efficient climate data; and predicts future climate variability over agroecological zones (AEZs). Eleven RCMs from CORDEX-Ethiopia were evaluated to simulate maximum temperature (Tmax), minimum temperature (Tmin) and rainfall variability over space and time, to validate their performance and to select the best-fit models. The temporal variability Tmax and rainfall was smaller with a CV of 10 %, indicating uniform distribution over stations. However, CV for Tmin over stations shows satisfactory distributions. Most of the evaluated RCMs have a better capability to capture the spatial variability of rainfall,Tmax, and Tmin in some stations. However, some of the RCMs can handle the spatial variability of Tmax while others are not. None of the RCMs can handle the spatial variability of Tmin in Debre Birhan, Majete, and Kewat AEZs. In general, most RCMs can capture the spatial and temporal variability of rainfall and temperature. It might be better to use the ensemble average of the output from these RCMs for precise estimation of data than specific RCMs. Therefore, this work could suggest the use of high-resolution RCMs for the projection of temperature and rainfall over different AEZs.

Performance evaluation of a high-resolution regional climate model in West Africa: sensitivity to land surface schemes

Performance evaluation of a high-resolution regional climate model in West Africa: sensitivity to land surface schemes

An increase in widespread extreme precipitation events during the northeast monsoon season over south peninsular India

While the spatio-temporal characteristics of Indian summer monsoon precipitation and its extreme spells have been extensively studied, the northeast monsoon, which occurs from October to December (i.e., post-monsoon season) and affects the southern peninsula of India, has not received as much attention. In light of this, the present study explores the spatio-temporal characteristics of precipitation during the northeast monsoon, with a particular emphasis on widespread extreme precipitation events and their associated large-scale synoptic systems, using recent ensemble of high-resolution regional climate models (RCMs) simulations and the Indian monsoon data assimilation and analysis (IMDAA) reanalysis. The study reveals that both models tend to underestimate the intensity and frequency of observed precipitation events, although their skills in reproducing the observed spatial patterns of both mean and extreme precipitation are quite high (r > 0.75). A substantial increase in widespread extreme precipitation events (nearly twofold), along with a 30% rise in precipitation intensity, has been observed in the recent decade compared to the 1980s, and models demonstrate a similar directional change but tend to underestimate the magnitude of observed precipitation. This increase appears to be linked to the rapid warming of the Indian Ocean, which, in turn, increases the water vapor in the atmosphere, ultimately supplying more moisture to the southeastern peninsular India. On the other hand, observed discrepancies in replicating some of the reported widespread impactful extreme precipitation events in the years 2007 and 2015 over the southern India region underscore the need for caution when interpreting model simulations. Low-pressure systems, such as troughs, associated with cyclonic circulations originating from the Bay of Bengal, have been identified as the primary sources of moisture fueling heavy precipitation during these events. Cluster analysis highlights varying synoptic patterns within the general framework, emphasizing the need for a more nuanced approach in simulating and forecasting extreme precipitation events. Overall, this study underscores the importance of enhancing modeling capabilities to better understand and prepare for the growing challenges posed by extreme precipitation events.