Precipitation Projections Research Articles

Identifying urban prone areas to flash floods: The case of Santa Cruz de Tenerife

Floods are the natural hazard that causes the largest annual losses in the world Urban expansion and population growth have made cities the most hazardous areas, mainly due to poor planning, occupation of the drainage network and soil sealing. Santa Cruz de Tenerife is one of the many cities worldwide threatened by this phenomenon. Its typically Mediterranean rainfall pattern, characterized by extreme precipitation events in short periods of time, together with its orography of steep ravines, short length and width, as well as its disorderly growth, make it a space prone to the occurrence of flash floods. In addition, the increase in torrential rainfall as a consequence of climate change and the tendency towards greater irregularity in precipitation is considerably intensifying the problem. This paper studies the characteristics of these episodes and the black spots inventoried in its General Management Plan (PGO for its initials in Spanish). On the other hand, flood modeling is carried out based on the rainfall characterization, whose maximum flows, in total, range between 500 and 1600 m3/s. Finally, a methodology that allows integrating both analyses to obtain a detailed hazard map is proposed as an alternative to the more traditional flood hazard analyses. A design storm of 288 mm is applied and the data is validated against the largest rainfall event on record, March 31, 2002. It has been shown that in an urban drainage network, the main watercourses and those that have disappeared due to urbanization represent areas susceptible to flooding and are the sectors that should be emphasized during the implementation of risk reduction measures. Finally, emphasis is placed on the need to integrate future climate projections of precipitation to better define the maximum flood flows.

Future projection of extreme precipitation using a pseudo-global warming method: A case study of the 2013 Alberta flooding event

The June 2013 extreme precipitation event in Alberta resulted in devastating flash floods that caused significant economic losses and societal disruption. In this study, two high-resolution experiments were conducted using the Weather Research and Forecasting (WRF) model to study the change of the 2013 Alberta extreme precipitation event in a warmer climate. The control experiment was forced with 6-hourly ERA-Interim reanalysis data, while the sensitivity experiment was forced with perturbed ERA-Interim reanalysis data with climate change signals derived from ten global climate models under the Representative Concentration Pathway 8.5 emission scenario. The results indicate that the 2013 Alberta extreme precipitation event is projected to exhibit two significant characteristics in a warming climate. First, precipitation is expected to increase over the Canadian Rocky Mountain region and eastern British Columbia. Second, the precipitation is expected to decrease over the Alberta and Saskatchewan Prairies. Future changes in the extreme precipitation event are associated with changes in the cyclone evolution, moisture transport, and atmospheric stability change caused by climate change. We also found that the increase in atmospheric stability due to the decrease of relative humidity in the lower atmosphere cause less precipitation to form over the plains and later enhance the orographic precipitation in the Canadian Rockies. In addition to the general increase of precipitable water under global warming, this mechanism causes the storm's precipitation to be more concentrated near the Canadian Rockies. The findings from this study could be beneficial for understanding future changes in extreme precipitation events that share similar characteristics.

Projection of precipitation under RCP4.5 and RCP 8.5 in central and southern regions of Iraq

Climate change has significant impacts on natural systems, especially in terms of altering hydrological patterns due to changing precipitation and melting snow and ice. This study examines projected changes in precipitation for central and southern Iraq under the RCP4.5 and RCP8.5 scenarios for the periods 2046–2065 and 2081–2100 using CMIP5 climate model HadGEM2-AO output. The results revealed that under RCP4.5, there was large spatial variation in the projected precipitation over the region during 2064-2065, ranging from as low as 27 mm in western part to as high as 1541 mm in the eastern part. The spatial variability as well as precipitation amount decreased considerably during 2081-2100 periods. Under RCP8.5 the projected precipitation was only 14-164 mm in 2046-2065 and 36-532 mm in 2081-2100 periods. Thus, under RCP8.5 scenario anticipated precipitation will be quite low.

Evaluation of Optimized Multi-Model Ensembles for Extreme Precipitation Projection Considering Various Objective Functions

Evaluation of Optimized Multi-Model Ensembles for Extreme Precipitation Projection Considering Various Objective Functions

Internally Driven Variability of the Angola Low is the Main Source of Uncertainty for the Future Changes in Southern African Precipitation

AbstractVariations in southern African precipitation have a major impact on local communities, increasing climate‐related risks and affecting water and food security, as well as natural ecosystems. However, future changes in southern African precipitation are uncertain, with climate models showing a wide range of responses from near‐term projections (2020–2040) to the end of the 21st century (2080–2100). Here, we assess the uncertainty in southern African precipitation change using five Ocean‐Atmosphere General Circulation single model initial‐condition large ensembles (30–50 ensemble members) and four emissions scenarios. We show that the main source of uncertainty in 21st Century projections of southern African precipitation is the internal climate variability. In addition, we find that differences between ensemble members in simulating future changes in the location of the Angola Low explain a large proportion (∼60%) of the uncertainty in precipitation change. Together, the internal variations in the large‐scale circulation over the Pacific Ocean and the Angola Low explain ∼64% of the uncertainty in southern African precipitation change. We suggest that a better understanding of the future evolutions of the southern African precipitation may be achieved by understanding better the model's ability to simulate the Angola Low and its effects on precipitation.

A Critical Evaluation and Future Projection of Extreme Precipitation Over South Korea in Observation-Based Products and a High-Resolution Model Simulation

A Critical Evaluation and Future Projection of Extreme Precipitation Over South Korea in Observation-Based Products and a High-Resolution Model Simulation

Rising groundwater levels in Dare County, North Carolina: implications for onsite wastewater management for coastal communities

ABSTRACT Onsite wastewater treatment systems (OWTS) are a common wastewater treatment approach in coastal communities. Vertical separation distance (VSD) requirements between the drainfield and groundwater aim to ensure aerated soils for wastewater treatment. When the VSD declines, OWTS can fail. This study evaluated groundwater response to sea level rise (SLR) and the implications for OWTS. A groundwater monitoring network (13 wells) was used to evaluate groundwater depth in Dare County, North Carolina. Groundwater levels were measured with water level meters and pressure transducers. Trends in groundwater depth and SLR were analyzed to evaluate the influence of SLR on groundwater depth. From 1984–2022, mean groundwater levels have risen (∼7.6 mm/year) in response to SLR. Currently, sites at <2.7 m land elevation are most likely to have groundwater depths <1 m and inadequate VSD. Based on current precipitation and NOAA intermediate SLR projections, groundwater depth projections suggest that OWTS at lower elevations are more likely to experience groundwater inundation by 2040–2060. SLR has resulted in reduced VSD causing diminished wastewater treatment capacity in low-lying areas. OWTS VSD requirements are typically static due to regulatory constraints. Future management approaches should consider adapting to rising coastal groundwater levels because of increasing wastewater contamination risks.

Podnebne projekcije temperature zraka in padavin za porečja Ledave, Pesnice in Vipave do konca 21. stoletja

As part of the project ‚CeVoTak‘, Integrated Management of Small Water Retention and Soil Erosion Prevention Measures in Agricultural Catchments, we studied changing temperature and precipitation conditions up to the year 2100. The study was conducted on agricultural lands in the catchments of the Ledava and Pesnica rivers in the sub-Pannonian region and the Vipava river in the sub-Mediterranean region. A common climate database was used to create the climate projections - RCM (Regional Climate Model) simulations from the project EURO-CORDEX and scenarios RCP (Representative Concentration Pathway), RCP2.6, 4.5 and 8.5. The projections were prepared for three time periods 2011-2040; 2041-2070 and 2071-2100 for 6 different regional climate models for average, minimum and maximum air temperatures and precipitation. Analysis of the ensemble of model simulations for all scenarios shows similar results for the basin of all rivers, an increase in temperature (maximum in winter, minimum in spring), with high confidence for all scenarios and periods. Projections of precipitation are less reliable, but show an increase in annual precipitation due to the winter increase. The use of climate change projections with expert interpretation is essential for determining the vulnerability of individual areas and building resilience through the implementation of climate change adaptation.

The combined impact of climate change scenarios and land use changes on water resources in a semi-arid watershed

This study proposes a combined approach involving climate change scenarios and varied land use and land cover (LULC) projections in a semi-arid watershed (El Grou) located in Morocco. The Soil and Water Assessment Tool (SWAT) model was coupled with land use scenarios generated using the Cellular Automata-Markov model for three dates (2030, 2040, and 2050) and future climate projections from the CMIP5 model under RCP 4.5 and 8.5 scenarios. Future precipitation and temperature projections indicate a decrease in annual precipitation by 5 % to 34 % and an increase in average temperatures, particularly during the summer months. LULC changes reveal a significant decline in agricultural lands and forests, with an expansion of bare soil by 2050. These changes, analyzed through three SWAT models representing different time periods, indicate a decrease in surface runoff by 41 % to 73 %, a reduction in total water yield by 21 % to 53 %, and a decline in groundwater recharge, posing significant challenges for water resource management and ecosystem health. The study underscores the need for integrated land and water management strategies, incorporating climate change adaptation measures, to ensure sustainable water resource utilization and build resilience in the El Grou watershed and similar semi-arid regions.

Climate change impact on Mediterranean viticultural regions and site-specific climate risk-reduction strategies

The global increase in extreme weather and climate events may dramatically impact agriculture, food safety, and socioeconomic dynamics. The Mediterranean basin is already exposed to extreme climatic events, severely challenging viticulture, a pivotal Mediterranean agro–industry. This study aims to understand better how climate is expected to evolve in six viticulturally important Mediterranean regions in Portugal, Italy, Turkey and Morocco, using a 4–member ensemble of climatic model projections under Representative Concentration Pathways (RCP) 4.5 and 8.5 for 2041–2070, and using the 1981–2010 period as a baseline. By comparing the main specific challenges these locations will face, we comparatively define the best strategies to reduce the impacts of climate change at the national and regional levels. Projections show increases in overall temperatures, up to + 3.6°C than the historical baseline, whilst precipitation projections indicate decreases that could reach 36% of the overall annual precipitation. Biological effective degree days, consecutive dry days, growing season length, tropical nights, or very heavy precipitation days, also show challenging prospects for viticulture in these countries. A screening of the adaptative strategies already undertaken in the studied countries suggests that growers are taking reactive rather than preventive strategies. Moreover, the discussion of the most suitable strategies in this study is region–specific, i.e., prioritised by the specific needs of each location. The conclusions drawn herein may support local growers, improving their decision–making based on the most adequate adaptive strategies to their conditions, thus optimising their sustainable production under changing climates.

Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble

Abstract. ​​​​​​​Severe floods with extreme return periods of 100 years and beyond have been observed in several large rivers in Bavaria in the last 3 decades. Flood protection structures are typically designed based on a 100-year event, relying on statistical extrapolations of relatively short observation time series while ignoring potential temporal non-stationarity. However, future precipitation projections indicate an increase in the frequency and intensity of extreme rainfall events, as well as a shift in seasonality. This study aims to examine the impact of climate change on the 100-year flood (HF100) events of 98 hydrometric gauges within hydrological Bavaria. A hydrological climate change impact (CCI) modeling chain consisting of a regional Single Model Initial-condition Large Ensemble (SMILE) and a single hydrological model was created. The 50 equally probable members of the Canadian Regional Climate Model version 5 large ensemble (CRCM5-LE) were used to drive the hydrological model WaSiM (Water balance Simulation Model) to create a hydro-SMILE. As a result, a database of 1500 model years (50 members × 30 years) per investigated time period was established for extreme value analysis (EVA) to illustrate the benefit of the hydro-SMILE approach for a robust estimation of HF100 based on annual maxima (AM) and to examine the CCI on the frequency and magnitude of HF100 in different discharge regimes under a strong-emission scenario (RCP8.5). The results demonstrate that the hydro-SMILE approach provides a clear advantage for a robust estimation of HF100 using the empirical probability of 1500 AM compared to its estimation using the generalized extreme value (GEV) distribution of 1000 samples of typically available time series sizes of 30, 100, and 200 years. Thereby, by applying the hydro-SMILE framework, the uncertainty from statistical estimation can be reduced. The study highlights the added value of using hydrological SMILEs to project future flood return levels. The CCI of HF100 varies for different flow regimes, with snowmelt-driven catchments experiencing severe increases in frequency and magnitude, leading to unseen extremes that impact the distribution. Pluvial regimes show a lower intensification or even decline. The dynamics of HF100 driving mechanisms depict a decline in snowmelt-driven events in favor of rainfall-driven events, an increase in events driven by convective rainfall, and almost no change in the ratio between single-driver and compound events towards the end of the century.

Performance and projections of the NEX‐GDDP‐CMIP6 in simulating precipitation in the Brazilian Amazon and Cerrado biomes

AbstractThe objective of this work is to provide projections of mean annual and monthly precipitation for the Brazilian Amazon and Cerrado biomes, in the near‐term (2021–2040), medium‐term (2041–2060) and long‐term (2081–2100). The intermediate and most pessimistic Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emissions scenarios were considered. Thus, 34 high‐resolution global climate models (GCMs) from the NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP) Phase 6 of the Coupled Model Intercomparison Project (CMIP6) were evaluated. The base period evaluated was from 1981 to 2010. The NEX‐GDDP simulations are bias‐corrected and spatially disaggregated. The Climate Hazards Group InfraRed Precipitation with Station v2.0 was chosen as the source of observed data due to low availability in situ data. The Kling‐Gupta efficiency (KGE) and the global performance indicator were implemented in Google Earth Engine to evaluate the GCMs. The results show that the GCMs perform satisfactorily, except for KACE‐1‐0‐G and IITM‐ESM. The median KGE is 0.86 for the biomes. Thus, the Ensemble Model of 32 GCMs (EM‐32) indicates a reduction in precipitation in the biomes, except the northern Cerrado. In the most pessimistic scenario, changes in annual precipitation range from 3% to −33% until the end of the century. The north‐central Amazon and the northwestern Cerrado are the most affected regions. In general, the monthly precipitations between September and November show the most intense decreasing rates. It is estimated that 91% and 23% of areas in the Amazon and Cerrado biomes, respectively, show robust signs of reduction in mean annual precipitation. Thus, EM‐32 shows more intense and robust climate projections, in comparison to the total annual precipitation of the subset of 33 raw CMIP6 models from Working Group I of the IPCC Sixth Assessment Report. Therefore, the EM‐32 precipitation projections can be applied to future hydrological and hydrosedimentological investigations.

Potential Near‐Term Wetting of the Southwestern United States if the Eastern and Central Pacific Cooling Trend Reverses

AbstractNear‐term projections of drought in the southwestern United States (SWUS) are uncertain. The observed decrease in SWUS precipitation since the 1980s and heightened drought conditions since the 2000s have been linked to a cooling sea surface temperature (SST) trend in the Equatorial Pacific. Notably, climate models fail to reproduce these observed SST trends, and they may continue doing so in the future. Here, we assess the sensitivity of SWUS precipitation projections to future SST trends using a Green's function approach. Our findings reveal that a slight redistribution of SST leads to a wetting or drying of the SWUS. A reversal of the observed cooling trend in the Central and East Pacific over the next few decades would lead to a period of wetting in the SWUS. It is critical to consider the impact of possible SST pattern trends on SWUS precipitation trends until we fully trust SST evolution in climate models.

Qualifying uncertainty of precipitation projections over China: mitigating uncertainty with emergent constraints

Predicting future mean precipitation poses significant challenges due to uncertainties among climate models, complicating water resource management. In this study, we introduce a novel methodology to mitigate uncertainty in future mean precipitation projections over China on a grid-by-grid basis. By constraining precipitation parameters of the Gamma distribution, we establish emergent constraints on parameters, revealing significant correlations between historical and future simulations. Our analysis spans the periods 2040–2069 and 2070–2099 under low-to-moderate and high emission scenarios. We observe reductions in uncertainty across most regions of China, with constrained mean precipitation indicating increases in monsoon regions and decreases in non-monsoon zones relative to raw projections. Notably, the observed 30%–40% increase in mean precipitation for the whole of China underscores the efficacy of our methodology. These observationally constrained results provide valuable insights into current precipitation projections, offering actionable information for water resource planning and climate adaptation strategies amidst future uncertainties.

STAR‐ESDM: A Generalizable Approach to Generating High‐Resolution Climate Projections Through Signal Decomposition

AbstractHigh‐resolution climate projections are critical to assessing climate risk and developing climate resilience strategies. However, they remain limited in quality, availability, and/or geographic coverage. The Seasonal Trends and Analysis of Residuals empirical statistical downscaling model (STAR‐ESDM) is a computationally‐efficient, flexible approach to generating such projections that can be applied globally using predictands and predictors sourced from weather stations, gridded data sets, satellites, reanalysis, and global or regional climate models. It uses signal processing combined with Fourier filtering and kernel density estimation techniques to decompose and smooth any quasi‐Gaussian time series, gridded or point‐based, into multi‐decadal long‐term means and/or trends; static and dynamic annual cycles; and probability distributions of daily variability. Long‐term predictor trends are bias‐corrected and predictor components used to map predictand components to future conditions. Components are then recombined for each station or grid cell to produce a continuous, high‐resolution bias‐corrected and downscaled time series at the spatial and temporal scale of the predictand time series. Comparing STAR‐ESDM output driven by coarse global climate model simulations with daily temperature and precipitation projections generated by a high‐resolution version of the same global model demonstrates it is capable of accurately reproducing projected changes for all but the most extreme temperature and precipitation values. For most continental areas, biases in 1‐in‐1000 hottest and coldest temperatures are <0.5°C and biases in the 1‐in‐1000 wet day precipitation amounts are <5 mm/day. As climate impacts intensify, STAR‐ESDM represents a significant advance in generating consistent high‐resolution projections to comprehensively assess climate risk and optimize resilience globally.

A CMIP6-ensemble-based evaluation of precipitation and temperature projections

Understanding climate change’s effects on dam basins is very important for water resource management because of their important role in providing essential functions such as water storage, irrigation, and energy production. This study aims to investigate the impact of climate change on temperature and precipitation variables in the Altınkaya Dam Basin, which holds significant potential for hydroelectric power generation in Türkiye. These potential impacts were investigated by using ERA5 reanalysis data, six GCMs from the current CMIP6 archive, and two Shared Socioeconomic Pathways (SSP2 − 4.5 and SSP5 − 8.5) scenario data. Four Multi-Model Ensemble (MME) models were developed by using an Artificial Neural Network (ANN) approach (ENS1), simple averaging (ENS2), weighted correlation coefficients (ENS3), and the MARS algorithm (ENS4), and the results were compared to each other. Moreover, quantile delta mapping (QDM) bias correction was used. The 35-year period (1980–2014) was chosen as the reference period, and further evaluations were conducted by dividing it into three future periods (near (2025–2054), mid-far (2055–2084), and far (2085–2100)). Considering the results achieved from the MMEs, variations are expected in the monthly, seasonal, and annual assessments. Projections until the year 2100 indicate that under optimistic and pessimistic scenarios, temperature increases could reach up to 3.11 °C and 5.64 °C, respectively, while precipitation could decrease by as much as 19% and 43%, respectively. These results suggest that the potential changes in temperature and precipitation within the dam basin could significantly impact critical elements such as future water flow and energy production.

Nonstationary multi-site design flood estimation and application to design flood regional composition analysis

Impacts of climate change and human activities may lead to changes in the spatiotemporal composition of the design flood as well as its size. Previous studies mainly focused on changes in design flood size, while there has been relatively little research on changes in its regional composition. In this study, a nonstationary multi-site design flood estimation method is developed, which is useful for the design flood regional composition analysis under nonstationary conditions. Dynamic copula models are first constructed to analyze the change in the joint distribution of the nonstationary multi-site flood variables with the consideration of the nonstationarity of the marginal distribution and copula structure parameters. Then the design flood combinations in multi-site for a specified design standard are calculated by comprehensively applying the equivalent reliability method, the expectation conditional and the most-likely conditional combination strategies, which considers the future precipitation change and design lifespan length impacts on the design flood. Finally, the uncertainty of the multi-site design flood estimation caused by the model parameters uncertainty is evaluated. A case study, based on the annual maximum 7-day (AM7) flood volume in the Yichang (YC) and Cuntan (CT) sites, is conducted to illustrate this method. Results show that flood quantiles in the YC and CT sites exhibit an increasing trend as the precipitation projections will increase in the future, but the flood quantiles in the YC site are less compared to the historical period because of the huge regulation and storage effect of the Three Gorges Reservoir. In addition, the design flood combination in the CT and YC sites are calculated and the CT design floods from the expectation combination strategy are bigger than those provided by the most-likely combination strategy.

Assessment of CMIP6 GCMs for selecting a suitable climate model for precipitation projections in Southern Thailand

The selection of General Circulation Models (GCMs) is critical due to computational limitations and underlying uncertainties. This study provides a comprehensive assessment of three bias correction (BC) methods, namely the delta change method (DT), quantile mapping (QM), and empirical quantile mapping (EQM). Utilizing precipitation data from 30 observation stations across Southern Thailand, the evaluation encompasses five CMIP6 GCM models (CAMS-CSM1-0, CanESM5, CNRM-CM6-1, CNRM-ESM2-1, IPSL-CM6A-LR). Evaluation metrics, root mean square error (RMSE), mean absolute error (MAE), Pearson's correlation (r), index of agreement (d), and mean bias error (MBE) are employed to assess BC methods. Evaluation measures suggest that the DT method outperforms EQM and QM, with higher accuracy and lower errors (DT: RMSE = 3.61, MAE = 2.32; EQM: RMSE = 3.82, MAE = 3.70). Taylor diagrams show that CNRM-ESM2-1 has the highest correlation across sites (r = 0.36), albeit with a wider dispersion, while CanESM5 has a more balanced performance. Significant annual precipitation increases are projected for different spans 2021-30, 2061-70, and 2091–2100, particularly under SSP585, which will influence flood risk, water management, and climate adaptation. Future projections with SSP585 continuously projecting a larger probability of increased precipitation. The DT method is recommended for the downscaling of CMIP6 GCMs for precipitation projections in Southern Thailand, recognizing its superior performance. The study's findings provide a foundation for informed decision-making and adaptation planning in southern Thailand, urging policymakers to prioritize climate resilience and adaptation strategies while using a multi-model ensemble approach for robust climate forecasts.

Climate Change Impacts on Precipitation Dynamics in the Southern Marmara Region of Turkey

Understanding the dynamics of precipitation patterns is crucial for effective water management strategies, especially in regions vulnerable to the impacts of climate change. This study investigates the projected changes in annual and seasonal precipitation across the Southern Marmara Region of Turkey by comparing the averages of the reference period (1971-2000) with those of the future period (2061-2090). Employing multiple climate models (GFDL, HADGEM, and MPI) and Representative Concentration Pathways (RCP4.5 and RCP8.5), the analysis includes Mann-Kendall trend tests and Sen's slope method to determine trends in precipitation patterns. Key findings reveal significant variability in precipitation projections among different models and scenarios, with implications for water resource management, agriculture, and ecosystem resilience in provinces such as Çanakkale, Balıkesir, Bursa, Bilecik, and Yalova. According to the annual rainfall change rates relative to the reference period, Balıkesir province stands out as the most resilient province against climate change with average rates of 8.81% and 7.09% under the HADGEM and MPI model simulations, respectively. Regarding seasonal variations, Bilecik province is expected to experience a significant decrease in rainfall, reaching up to -53.78% under the MPI RCP8.5 scenario. In terms of within-period changes in annual rainfall values, the strongest declining trend was identified with Z=-2.03 in Bilecik province under the MPI RCP8.5 scenario conditions by the Mann-Kendall test. On the other hand, for seasonal variations, Bursa province demonstrates the most robust decreasing trend under the GFDL RCP4.5 conditions (Z=-2.89). The study emphasizes the importance of considering spatially varying precipitation patterns and potential shifts in atmospheric circulation for sustainable water resource management amidst climate variability and change in the Southern Marmara region. These findings provide critical insights for policymakers and stakeholders involved in developing adaptive strategies to address the challenges posed by future climate scenarios.

The difference in the uncertainty sources between future projections of mean and extreme precipitation over East Asia

As the incidence of extreme precipitation events attributable to global climate change increases, providing policymakers with accurate model predictions is of the utmost importance. However, model projections have inherent uncertainties. The present study attempted to distinguish the sources of the uncertainty of the mean and extreme precipitation projections in the East Asia region using the mean boreal summer precipitation, simple precipitation intensity index (SDII), maximum cumulative 5 day precipitation, and annual maximum daily precipitation (Rx1d). The results show that while the mean precipitation was projected to change very little regardless of the scenario, more extreme indices were projected to increase considerably by the end of the century, particularly in the high-emissions scenarios. On average, model uncertainty accounted for the largest part of the uncertainty. However, for Rx1d in the 2030s, as well as mean and SDII in some regions until the 2060s, the internal variability was the largest contributor. In addition, whilst scenario uncertainty accounted for a negligible proportion of average precipitation variability, for the more extreme the precipitation indices, scenario uncertainty contribution to total variability by the end of the century was significant; namely, the scenario uncertainty contribution was overall highest for the maximum one-day precipitation. Additionally, comparatively wetter regions had greater overall projection uncertainties, especially uncertainty arising from internal variability, likely due to the influence of interannual variability from the EA summer monsoon.