Indian Rainfall Research Articles

A machine learning-based approach for flash flood susceptibility mapping considering rainfall extremes in the northeast region of Bangladesh

Flash floods are catastrophic global events, especially in northeast Bangladesh, and assessing flash flood susceptibility is crucial for preparedness and mitigation. Traditional geographic system-based flash flood susceptibility mapping struggles to capture flash floods’ non-linear and complex nature. However, machine learning models have recently emerged as an efficient alternative to address these limitations. This study evaluates four machine learning models—Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Random Forest – Gradient Boosting (RFGB) Hybrid, and Categorical Boosting (CatBoost)—for flash flood susceptibility. It categorizes areas into five susceptibility levels: very low, low, moderate, high, and very high. Covering 24,424.25 km2 across eight districts, the study uses 400 points (200 flood and 200 non-flood) for training and validation, based on field investigation, historical flood information, Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar data using Google Earth Engine, and insights from the local people. The models’ predictive performances are evaluated by incorporating topographical attributes and rainfall indices and using accuracy, precision, recall, F1 score, and Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) score, with 70% of the data for training and 30% for testing. The ANN model performs best with rainfall indices, achieving high accuracy (maximum AUC = 0.802). The RFGB hybrid model shows excellent training accuracy (AUC ≥ 0.971) but suffers from overfitting during validation (AUC ≤ 0.674), requiring careful hyperparameter adjustment. The CatBoost model effectively uses both rainfall indices and terrain features, achieving AUC = 0.701 in training and AUC = 0.667 in validation. The ANN model conservatively includes the largest area (2198.3 km2) under ’very high’ susceptibility. This study’s flash flood susceptibility maps, which include rainfall extremes, are more robust than those without, helping local administrative authorities and national flood practitioners prepare for flash floods.

Projected changes of thermal, rainfall and drought indices in Morocco from high resolution climate models

Abstract Over the past decade, global changes in temperature, average and extreme precipitation have been observed many times. The increase in temperature or even the change in precipitation systems is directly affect extreme weather events, for example, heat waves, heavy rainfall, storms, and droughts are becoming more frequent and more powerful worldwide due to climate change caused by several factors including human activity. This is why there is a necessity to monitor future changes in climate extremes. The objective of this paper is to evaluate the future changes in thermal and rainfall extremes projected by the four climate models over time horizon 2041-2060 relative to the 1981-2010 reference period, based on the 5 climate indices, including Tmm, Tx90p, WSDI, PRCPTOT and SPI. The projections are given under the RCP 4.5 medium scenario. The analysis in thermal aspect shows that Morocco could experience by 2041-2060, a significant warming, has manifested by an increase in average temperature. Future changes in rainfall indices show that the spatial distribution of rainfall in Morocco could change significantly by 2041-2060 compared to the reference period 1971-2010. The four climate models agree on a decrease in annual precipitation that could affect most of the Kingdom. In the same way to dryness, the future changes of the SPI index that characterizes the drought, indicates a move to a dry climate.

Assessment of shift in GWPZs in Kashmir Valley of Northwestern Himalayas

Groundwater is a critical resource for the Kashmir Valley, which is increasingly pressured by urbanization and climate change. This study aims to delineate Groundwater Potential Zones (GWPZs) in the Kashmir Valley using the Analytical Hierarchy Process (AHP) and Geographical Information Systems (GIS). The research integrates eight thematic layers, including geology, geomorphology, slope, drainage density, land use/land cover (LULC), lineament density, rainfall, and topographic wetness index (TWI), to assess GWPZs for 1995, 2010, and 2020. Weights for each layer were assigned based on their influence on groundwater dynamics. The generated GWPZs were classified into five classes: very high, high, moderate, low, and very low. Over 25 years, significant changes in GWPZs were observed: very low potential zones decreased by 45.17%, low potential zones by 26.17%, and very high potential zones by 72.95%. Conversely, moderate potential zones increased by 50.87%, and high potential zones saw a slight increase of 9.00%. Results indicated that high and very high GWPZs are predominantly located in the valley's alluvial plains, primarily covered by agricultural and partially horticultural land. Validation of the generated GWPZs using bore well locations, discharge data, and Receiver Operating Characteristic (ROC) curves demonstrated high accuracy, with Area Under the Curve (AUC) values of 0.915, 0.898, and 0.890 for the respective years. These findings underscore the adverse impact of urbanization on groundwater potential zonation and emphasize the need for sustainable water management practices. This study provides valuable insights into the long-term shifts in groundwater potential in response to urban expansion and climate change. It highlights the importance of continuous monitoring and adaptive management to safeguard groundwater resources.

Cirrus cloud occurrence and its geometrical and optical properties during the transient monsoon conditions

Knowledge of variation in the percentage occurrence of the cirrus clouds (POC) during transient monsoon conditions is essential for understanding the role of the monsoon in transporting the water vapor into the lower stratosphere which is vital in quantifying the radiation budget of the earth–atmosphere system. In this paper, we present the spatial structure of the POC, the geometrical properties such as cloud top and base height (CTH & CBH), cloud thickness (CTH-CBH), optical properties such as optically thick, thin, and subvisible cirrus clouds during the active and break phases of the Asian summer monsoon using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) during July–August 2006–2018. The active and break phases are identified based on the central India rainfall from the India Meteorological Department dataset. The POC is found to be higher by 10–30 % over central India and some parts of the eastern Arabian Sea (AS) and Bay of Bengal (BoB) during the active phase compared to the break phase which shows enhancement in POC over the foothill of Himalaya and southern part of BoB and AS. The higher POC is attributed to the increase in the convective activities associated with the increase in tropical easterly jet during the active phase. The CTH and CBH range ~14–18 km and 12–16 km, respectively during the active phase compared to the break phase. The contributions of optically thick and thin clouds are higher in POC during both phases compared to subvisible clouds. Both convectively generated and in situ-formed cirrus clouds dominate during the active phase compared to the break phase indicating the dominance of the freeze-drying processes during the active phase.

Trends of Extreme Temperature and Rainfall Indices for Addis Ababa City, Ethiopia

The extreme temperature and precipitation index trend analysis for Addis Ababa city is presented in this study. The Bole and Tikur Anbesa observatories, two stations in the capital city of Addis Ababa, provide the temperature and rainfall variation used in this analysis. The RClim-Dex software has been used to find climate extreme indices for Addis Ababa city stations based on climate data for the period 1954 to 2019. The annual total precipitation trend (PRCPTOT) at the 5% significant level exhibits a positive slope at the Addis Ababa observatory and a negative slope at the Addis Ababa Bole station. SDII trends indicate an increase at the 5% significant threshold for both stations. At the 5% significance level, the trends for both stations’ maximum and lowest temperatures show a positive slope. Conversely, both stations exhibit a negative slope in the diurnal temperature ranges. The RClim-Dex results indicate that, while rainfall intensity is decreasing, there is a tendency for annual rainfall indices to increase at the Addis Ababa observatory station, but to decrease at the Bole station. Additionally, there is an upward slope for consecutive wet days for all stations. Nevertheless, steady warming patterns are evident for both the maximum and minimum temperature indices. In contrast, declining trends are shown in the diurnal temperature ranges.

Predisposition to Mass Movements on Railway Slopes: Insights from Field Data on Geotechnical and Pluviometric Influences

Monitoring natural slopes, embankments, and unstable slopes is crucial to reducing predisposition to mass movements, especially in areas with geotechnical instability and high rainfall. This study proposes a methodology to identify geotechnical and pluviometric triggers of mass movements in railway slopes. It involves registering slopes and embankments along the railroad, recording accumulated rainfall indices, and documenting associated accidents. The experimental program included a cadastral survey at a pilot site on the MRS company’s railway network in the Paraopeba branch, Minas Gerais, Brazil. Surface and subsurface drainage conditions, anthropic interventions, and modifications affecting slope stability were also examined. Additionally, the history of accidents involving geotechnical and regional rainfall indices were incorporated to identify potential triggering events for mass movements. The study found a good correlation between landslide records and geotechnical risk mapping but a low correlation between landslide records and rainfall isohyets. The latter result is attributed to the low density and poor distribution of rainfall data and active pluviometers in the region. Overall, understanding the geological–geotechnical characteristics of slopes and the correlation between accidents and rainfall indices provides valuable insights for predicting potential landslide occurrences.

Performance assessment of data-driven models for sky luminance prediction in Indian tropical climate

The precise prediction of sky luminance distribution is inevitable for design of efficient daylighting systems. The lack of measured sky luminance data in developing countries like India makes the current standard design sky models redundant. Most of the designer use Perez sky model for prediction of sky luminance distribution using solar insolation conditions for such locations. Data-driven models using machine learning can be a possible solution for prediction of sky luminance distribution in such locations. The present study compares the prediction accuracy of such data-driven models using four different machine learning techniques and compares the results Perez sky model. It becomes clear from the results that the data-driven model does not perform well when the location is different from the training dataset location, and the Perez sky model gives better results for such cases.

Debris‐flow fan channel avulsions: An important secondary erosional process along the Ichino‐sawa torrent, Japan

AbstractSediment transported from debris‐flow initiation zones is typically stored in a topographic feature called a debris‐flow fan, the formation process of which governs secondary sediment transport further downstream. Although sediment transport from debris‐flow fans can impact sediment regimes and change landforms, the determinants of progressive fan formation are not well constrained. To identify such determinants, this study monitored debris flows and performed topographic surveys of debris‐flow fans in the Ichino‐sawa torrent (Japan) during 2016–2017. In this period, eight debris flows occurred, two of which eroded the existing fan and formed a new channel with a short recurrence interval (~40 days). Consequently, these two cases induced substantial sediment transport further downstream from the fan. The examined rainfall indices did not provide a threshold for diagnosing the occurrence of such sediment transport. Debris flows with a large flow depth and a long duration led to changes in the runout direction and subsequently formed new channels. Before these processes, the existing channel was backfilled and plugged by previous debris flows, forming a steep fan surface around the fan apex. The results suggest that increasing the magnitude and the duration of debris flows potentially triggers sediment transport from fans coupled with channel plugging. The annual sediment transport from the fan exceeded almost all sediment yields of the world rivers and was found comparable with that linked with volcanic eruptions and their aftermath. Thus, the fan‐formation process can induce substantial sediment transport, independent of volcanic perturbations and extreme climatic events, and is dependent on the sediment supply from repeated occurrence of debris flows in the initiation zones.

Identifying climatic risks and relevant adaptation strategies for selected smallholder farming regions, Limpopo Province, South Africa

ABSTRACT Due to low adaptive capacities and high reliance on weather-sensitive natural ecosystems for their livelihoods and food production, smallholder farmers are highly vulnerable to risks associated with climate change and variability. Such risks are location-specific; thus, to cope with them, smallholder farmers require tailored adaptation strategies. To support these farmers, this study aimed to identify climatic risks threatening sustainable smallholder farming in the Limpopo Province regions of Gavaza, Ga-Makanye and Giyani. Weather station records spanning up to 1980–2021 were used to identify climatic risks, for the October–April dryland maize growing season. Risks were identified based on temporal trends and/or interannual variability patterns for reference evapotranspiration and a suite of agriculturally relevant rainfall and temperature indices. Based on the identified risks, tailor-made adaptation strategies were devised for application by farmers within the study region. The results revealed the specific risks of growing seasons becoming shorter and increasingly hotter with highly irregular rainfall patterns. To adapt and improve agricultural productivity despite these prevailing climatic risks, farmers within the study region will benefit if they plant drought tolerant, early maturing and higher yielding crop varieties, apply soil water conservation techniques and make use of seasonal and daily weather forecasts to guide their decision-making.

Projected Changes in Rainfall Extremes over West African Cities Under Specific Global Warming Levels Using CORDEX and NEX-GDDP Datasets

We analyze long-term extreme events in daily precipitation for the period of 1950–2005 and future projections from 2006 to 2100 based on representative concentration pathways (RCP8.5) over West Africa, including twelve major cities of the subregion. The study uses two datasets from the Coordinated Regional Downscaling Experiments (CORDEX) and NASA Earth eXchange Global Daily Downscaled Projections (NEX–GDDP), for their potential to simulate extreme rainfall characteristics over West Africa. CORDEX datasets are dynamically downscaled from 9 General Circulation Model (GCM) and the NEX-GDDP is statistically downscaled from 21 GCM simulations from CMIP5. The ensemble members of each dataset were used for the analysis and evaluated with eight observations. The projected changes in the rainfall indices were analyzed at four Global Warming Levels (GWLs) under the RCP8.5 climate-forcing scenario. The projections from the CORDEX and NEX-GDDP ensembles were compared. Self-Organizing Map (SOM) analysis was used to classify the projected changes from the simulations into twelve groups, based on their characteristics. Both CORDEX and NEX-GDDP models perform well in simulating the frequency and intensities of daily rainfall characteristics for important parts of the domain. However, substantial differences exist between the models and their relative performance varies with the indices. In all cases, NEX-GDDP outperforms CORDEX in stimulating the extreme indices. On the projections, CORDEX showed a wider spread than the NEX–GDDP for RTOT and vice versa for R97.5pTOT for all the GWLs.

Western disturbances: Occurrence and impact on wheat productivity in Punjab

Western disturbances (WD) bring moderate to heavy rain in the northern parts of the country as well as heavy snow to the mountain areas of the Indian subcontinent. It is the source of most of the winter and post-monsoon rainfall in northwest India. Spatio-temporal analysis of western disturbances was carried out by using weather data recorded at Ballowal Saunkhri (sub-mountainous region), Ludhiana (central plain region) and Bathinda (south west region). The WD events per month were lowest in November and highest in March month averaged over all the agroclimatic regions. The highest numbers of WD events were found during February month in the Sub-mountainous region and during March in central plain region and south-western region. Winter rainfall quantity and number of rainfall events were higher in the sub-mountainous region followed by the central plain region and lowest in the south-western region. Higher number of WD during wheat growing season along with less and well distributed rainfall resulting in lower maximum temperature conditions were conducive for higher productivity in Punjab. The significantly positive correlation was observed between the number of western disturbances and wheat productivity during April month at central plain region and southwest region, whereas, the seasonal total number of WDs showed significant positive relationship at submontane region and southwest region.

Trends in extreme rainfall and their relationship to flooding episodes in Vhembe district, South Africa

Extreme weather events, such as heavy rainfall, are being increased by climate change in various regions, and such events often cause floods. This study examined the trends and variability of extreme rainfall indices using daily rainfall data (1981–2023) from three study sites at different socio-economic development spectra in Vhembe District, Limpopo Province, South Africa. The analyses focus on indices such as the annual total rainfall from wet days (PRCPTOT), the maximum number of consecutive dry days (CDD) and wet days (CWD), annual maximum 1-day and 5-day rainfall (RX1 day and RX5 day), the Simple Daily Intensity Index (SDII), the number of days exceeding varying amounts of precipitation (R10, R20, R40) and the annual number of wet days with rainfall greater than the 95th and 99th percentile (R95p and R99p) of the 1981–2023 daily rainfall. We discuss the observed trends in extreme rainfall indices in light of the actual flood occurrences to establish linkages. Several statistically significant and marginal changes in extreme rainfall trends were identified and provided key insights into reported flooding events in the district—flooding episodes were mainly attributed to the significant increases in total precipitation (PRCPTOT) and rainfall exceeding the 99th percentile of daily rainfall (R99p). Other significant contributors were declining CDD and increasing RX1day at Duthuni, increasing R40 at Musina as well as increasing R1 and declining CDD at Sane. However, the low altitude, urbanization, poor waste management and inadequate drainage systems were among the key non-climatic drivers of flood risk across the study sites, but these warrant further investigation. The complex interplay between climatic and non-climatic drivers of flood risk underscores the importance of localized climate studies and the need for adaptive strategies to minimize loss and damage. Overall, this research provides valuable insights into localized extreme rainfall trends, which are essential for developing site-specific flood mitigation and adaptation strategies. However, such initiatives require placing vulnerable communities at the centre in order to develop solutions that are locally led and relevant.

Response to hydrometeorological changes and evaluation of habitat quality in the Dongting Lake basin, China

ABSTRACT With the advancement of hydraulic engineering processes, surface water resources are encountering unprecedented crises that threaten social and ecological sustainability. This study analyzed the response changes and potential correlations between river conditions and climate change in the Dongting Lake (DTL) basin using gray correlation analysis and the LSTM model. Additionally, the InVEST model was employed to further assess the distribution of habitat quality within the basin. The results of the study showed that (1) the range of variation of environmental flows in each tributary became significantly narrower and the frequency of extreme hydrological conditions was reduced in the period after the abrupt hydrological change. (2) The hydrological status had a higher degree of correlation with the rainfall index, and the performance is more prominent on the Xiang River, and the influence of temperature is less. (3) Except in the Xiang River Basin, human activities are the dominant factor of runoff variation, and the annual contribution is generally higher than 50%. (4) Habitat quality in the ‘high’ distribution was the most guarded and always accounted for more than 60% of the basin, while habitat quality in the east-central part of the DTL basin and around the DTL was generally low.

Developing a flood risk assessment model with genetic algorithm-based weights

To reduce flood risk efficiently within constrained disaster prevention budgets, governments employ economic analyses and qualitative flood risk assessments. However, conventional methods, such as entropy weight and the Analytic Hierarchy Process method, have limitations in terms of the accuracy of the flood risk index. Here, we overcome these limitations by applying a genetic algorithm (GA) – an optimization method mimicking a natural selection process and biological genetic evolution. We developed a new flood risk index by using GA to calculate weights to indicators associated with four items (Hazard, Exposure, Vulnerability, and Capacity) for 161 Korean cities and counties from 2016 to 2021. The indicators (number of buildings, farmland area, dependent population, etc.) for the Exposure and Vulnerability items were reflected in the evaluation only for damaged targets directly exposed to flood risk, using grid cells of indicators overlaid on the flood risk map. Our GA-based method aimed to optimize each indicator’s weights to minimize errors between damage rankings and flood risk index rankings. Results show that our method reduced errors by 21.42 % during 2016–2021, outperforming traditional methods. Therefore, it is easy to identify municipalities that lack disaster prevention capabilities and are vulnerable to flood risk by comparing flood risk indices under the same conditions, such as maximum rainfall index. Our proposed method could better aid local government in decision-making for flood risk mitigation by allocating constrained budgets efficiently.

Impact of Enso on Indian Monsoon and Food Production

El Nino is a big ocean-atmosphere climate interaction It happens when Sea Temperatures (SST) warm up in central and eastern parts of the Equatorial Pacific. Whereas La Nina is the opposite event of it, when ocean SST cools down in those same areas. Together, El Nino and La Nina are known as the El Nino Southern Oscillation Index (O). Usually, El Nino brings warm and dry weather to southern & eastern inland regions of India, Australia, Indonesia, Philippines, Malaysia, and the central Pacific islands. The variations of Indian summer monsoon Rainfall (ISMR) are linked to changes in sea surface temperatures over the equatorial Pacific & Indian Oceans. ENSO events really affect summer rainfall in India and major droughts have faced during El Nino events. When El Nino kicks in, it often leads to weak monsoons & higher temperatures in India. This raises the chances of droughts, which could upset crop production & water supply in India. The lack of rain because of El Nino causes crop production to drop, especially short-duration Kharif crops. This situation can lead to higher inflation rates & slower GDP growth, since agriculture is such a huge part of India’s economy. Therefore, this paper provide information about how El Nino affects climate throughout various regions of India, especially regarding monsoons and growing food.

“I Will Still Fight for It till the End”: Factors That Sustain and Detract from Indian Youths’ Climate Activism

This qualitative study utilized semi-structured interviews with 22 Indian youth climate activists (mean age = 19) from 13 cities across India to explore factors that sustain or detract from climate activism. Data were analyzed using reflexive Thematic Analysis. In addition to two gatekeeping factors (lack of family support and financial and job pressures) that may prevent youth from joining or cause youth to quit their climate activism entirely, this study identified factors that affected youth’s sustained climate activism: two detracting factors (negative interactions with government, and online and in-person bullying and harassment); four supporting factors (sense of community, internal motivations, seeing tangible achievements, and personal growth); and one factor (climate anxiety) with mixed effects on sustaining youth activism. Further, we describe ways in which youth holding minoritized identities, including lower-income, Muslim, and rural youth, had distinct experiences.

Dendrochronological Analysis of Pinus pinea in Central Chile and South Spain for Sustainable Forest Management.

Pinus pinea is an important Mediterranean species due to its adaptability and tolerance to aridity and its high-quality pine nuts. Different forest types located in Mediterranean native and non-native environments provide the opportunity to perform comparative studies on the species' response to climate change. The aims of this study were to elucidate growth patterns of the species growing in native and exotic habitats and to analyze its response to climatic fluctuations, particularly drought, in both geographical contexts. Understanding stone pine (Pinus pinea) growth responses to climate variability in native and exotic habitats by comparing natural stands and plantations may provide useful information to plan adequate management under climate change. By doing so, we enhance the understanding of P. pinea's adaptability and provide practical approaches to its sustainable management. In this study, we reconstructed and compared the stem radial growth of seven stone pine stands, two in southern Spain and five in central-southern Chile, growing under different climatic conditions. We quantified the relationships between growth variability and climate variables (total rainfall, mean temperature, and SPEI drought index). Growth was positively correlated with autumn rainfall in plantations and with autumn-winter rainfall in natural stands. Growth was also enhanced by high autumn-to-spring rainfall in the driest Chilean plantation, whereas in the wettest and coolest plantation, such correlation was found in winter and summer. A negative impact of summer temperature was found only in one of the five Chilean plantations and in a Spanish site. The correlation between SPEI and tree-ring width indices showed different patterns between and within countries. Overall, exotic plantations showed lower sensitivity to climate variability than native stands. Therefore, stone pine plantations may be useful to assist in mitigating climate change.

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.

Volumetric and energy production assessment of wood in managed forest in the Brazilian arid biome

There is a growing demand for wood products from forests located in dry regions, which includes the Caatinga, a biome in Northeast Brazil. This study evaluates the relationship between volumetric production, energy potential, and the rotation cycle. Information was collected from forest stands in different stages of regeneration located in an arid region of Brazil. Based on the forest management plan, four fields were selected with post-logging ages of 9, 11, 13, and 16 years. This inventory recorded circumference at chest height, circumference at base height, total height, volume, stored energy, technical cutting age, and rainfall index. The results showed that the species that presented the most significant quantity of stems did not always correspond to those that obtained the most significant amount of biomass. The technical cutting age was determined at 16 years, aiming to maximize wood utilization. Regarding energy density, the 9-year-old field reached 7,281 kcal ha-1, the 11-year-old field obtained 14,448 kcal ha-1, the 13-year-old field recorded 41,526 kcal ha-1, and the 16-year-old field reached 98,190 kcal ha-1. The species that contributed most to energy accumulation included Mimosa tenuiflora with 3,740 kcal m–³, Piptadenia stipulacea with 3,271 kcal m-3, and Cenostigma pyramidale with 3,101 kcal m-3.

Trend and inter-annual variability in regional climate models – Validation and hydrological implications in southeast Australia

We assessed the ability of Regional Climate Models (RCMs) to reproduce observed means, inter-annual variance and trends for rainfall indices that were important for runoff generation over southeast Australia. To establish the benefit of the RCM without being penalized or rewarded by the performance of the forcing GCM, we used ECMWF Re-Analysis-interim (ERA-Interim) to force the Weather Research and Forecasting model (WRF) and the Conformal Cubic Atmospheric Model (CCAM). The performance of two different configurations of each RCM was evaluated against an observational dataset (Australian Gridded Climate Data, AGCD) and compared with that of ERA-Interim. The assessments were carried out at both observational and ERA-Interim grid resolutions: ∼5 km and ∼80 km, respectively. As hypothesised, the RCMs outperformed ERA-Interim in representing spatial patterns and magnitude of mean rainfall, because of higher spatial resolution. RCMs also accurately represented the general spatial patterns of variance, but systematically underestimated the inter-annual variability over much of the domain. RCMs performed better than ERA-Interim in reproducing the magnitude of trends in some cases, especially in the decline of cool season rainfall in southeast Australia, which has had significant impacts on water resources. One of the two CCAM runs generally performed best across all rainfall indices, in part because of atmospheric spectral nudging and an improved land-surface model. Based on the findings, we have provided suggestions on where new research on the development of RCMs could be focused and recommendations relevant to the next generation of Australian hydro-climate projections generated from the sixth phase of the Coupled Model Intercomparison Project (CMIP6).