Sub-daily Rainfall Research Articles

Evaluation of fine resolution gridded rainfall datasets over a dense network of rain gauges in Niger

This study evaluates the reliability of gridded rainfall products with fine spatial resolution using 29 gauges of a dense observation network in Niger. Four gridded datasets were considered, viz. the African Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), Climate Prediction Center morphing technique (CMORPH) and Tropical Applications of Meteorology using Satellite data and ground-based observations (TAMSAT). The datasets were quantitatively compared to the gauge rainfall at different timescales and their rainfall detection skills were assessed using four validation metrics and three contingency indices, respectively. Furthermore, their potentials for trend analysis was assessed using a modified Mann-Kendall test; and finally, their capability of capturing damaging rainfalls was evaluated using data of five historical damaging rainfall amounts on a daily basis. The CMORPH product showed a good performance in detecting rainfall events and exhibited the best linear relationship with the gauge rainfall; however, it displayed high errors and relative bias (RB > 100%)—showing its inaccuracy in rainfall quantification. At daily, dekadal and monthly timescale, CHIRPS showed the lowest errors, followed by the ARC2 and TAMSAT, whereas ARC2 exhibited the lowest RB—between −12% and 22%. All of the four datasets were unable to unveil the significant trends found in ground observations and were inaccurate in sensing the depths of damaging rainfall amounts. The overall results revealed that the 3-hourly CMORPH dataset is a good sub-daily rainfall product—whereas daily, dekadal and monthly CHIRPS and ARC2 rainfalls have good potentials for hydro-climatological applications in Niger.

Estimation of Regional Sub-Daily Rainfall Ratios Using SKATER Algorithm and Logistic Regression

Developing Intensity-Duration-Frequency (IDF) curves is a paramount input in stormwater systems design. To construct these IDF curves, rainfall records at sub-daily durations, provided by continuous rainfall recorders, are required; however, these recorders are seldom available in many locations of interest. To fill this gap, available meteorological and topographical information for a study area in Saudi Arabia are investigated to get an estimate of the ratios of sub-daily rainfall depths to the 24-h depths (sub-daily ratios or SDRs), via applying the following methodology. A spatially constrained regionalization approach is implemented, using the SKATER algorithm, based on 60 gauging stations, to form regions of contiguous stations, based on the similarities of their SDRs. Four different regions are formed, where each region shows consistent SDRs; yet distinctly different from other regions. Subsequently, a multinomial logistic regression model is built and trained, with commonly available meteorological and topographical information as explanatory variables, to determine to which region a specific location belongs. The model is validated based on a hold-out validation method and assessed through confusion matrix statistics to evaluate the model performance. The model shows high performance in predicting the correct regional SDR and it is extended to produce a gridded map covering ungauged areas. Based on this procedure, one can develop the IDF curve for any location within the study area, even if there is no rainfall recorder in that location.

Changes in seasonality and magnitude of sub-daily rainfall extremes in Emilia-Romagna (Italy) and potential influence on regional rainfall frequency estimation

Study regionThe present study focuses on Emilia-Romagna (northern Italy), a densely populated region affected in the last few years by several destructive flood events. Study focusA rich and detailed regional dataset of annual maximum series (AMS) of sub-daily rainfall is considered with a three-fold aim: to detect possible changes in (1) seasonality and (2) magnitude of sub-daily rainfall extremes, (3) to assess the current reliability and accuracy of a previously developed regional model for rainfall frequency estimation based on the local value of mean annual precipitation (MAP). New hydrological insights for the regionThe main findings of the study include: (1) delay (towards Autumn) in the mean timing of sub-daily rainfall extremes, more marked for higher elevations and longer durations; (2) significant increase in the magnitude of sub-daily extreme events for some areas, especially in the Apennine; (3) high sensitivity of the regional predictions of the frequency regime of sub-daily rainfall extremes to local changes in AMS and MAP.

A Robust Method to Update Local River Inundation Maps Using Global Climate Model Output and Weather Typing Based Statistical Downscaling

Global warming is changing the magnitude and frequency of extreme precipitation events. This requires updating local rainfall intensity-duration-frequency (IDF) curves and flood hazard maps according to the future climate scenarios. This is, however, far from straightforward, given our limited ability to model the effects of climate change on the temporal and spatial variability of rainfall at small scales. In this study, we develop a robust method to update local IDF relations for sub-daily rainfall extremes using Global Climate Model (GCM) data, and we apply it to a coastal town in NW Spain. First, the relationship between large-scale atmospheric circulation, described by means of Lamb Circulation Type classification (LCT), and rainfall events with potential for flood generation is analyzed. A broad ensemble set of GCM runs is used to identify frequency changes in LCTs, and to assess the occurrence of flood generating events in the future. In a parallel way, we use this Weather Type (WT) classification and climate-flood linkages to downscale rainfall from GCMs, and to determine the IDF curves for the future climate scenarios. A hydrological-hydraulic modeling chain is then used to quantify the changes in flood maps induced by the IDF changes. The results point to a future increase in rainfall intensity for all rainfall durations, which consequently results in an increased flood hazard in the urban area. While acknowledging the uncertainty in the GCM projections, the results show the need to update IDF standards and flood hazard maps to reflect potential changes in future extreme rainfall intensities.

Comparison of Local, Regional, and Scaling Models for Rainfall Intensity-Duration-Frequency Analysis.

Intensity-duration-frequency (IDF) analyses of rainfall extremes provide critical information to mitigate, manage, and adapt to urban flooding. The accuracy and uncertainty of IDF analyses depend on the availability of historical rainfall records, which are more accessible at daily resolution and, quite often, are very sparse in developing countries. In this work, we quantify performances of different IDF models as a function of the number of available high-resolution (N τ ) and daily (N 24h) rain gauges. For this aim, we apply a cross-validation framework that is based on Monte Carlo bootstrapping experiments on records of 223 high-resolution gauges in central Arizona. We test five IDF models based on (two) local, (one) regional, and (two) scaling frequency analyses of annual rainfall maxima from 30-min to 24-h durations with the generalized extreme value (GEV) distribution. All models exhibit similar performances in simulating observed quantiles associated with return periods up to 30 years. When N τ >10, local and regional models have the best accuracy; bias correcting the GEV shape parameter for record length is recommended to estimate quantiles for large return periods. The uncertainty of all models, evaluated via Monte Carlo experiments, is very large when N τ ≤ 5; however, if N 24h ≥ 10 additional daily gauges are available, the uncertainty is greatly reduced and accuracy is increased by applying simple scaling models, which infer estimates on subdaily rainfall statistics from information at daily scale. For all models, performances depend on the ability to capture the elevation control on their parameters. Although our work is site specific, its results provide insights to conduct future IDF analyses, especially in regions with sparse data.

Physically based model for gully simulation: application to the Brazilian semiarid region

Abstract. Gullies lead to land degradation and desertification as well as increasing environmental and societal threats, especially in arid and semiarid regions. Despite this fact, there is a lack of related research initiatives. In an effort to better understand soil loss in these systems, we studied small permanent gullies, which are a recurrent problem in the Brazilian northeastern semiarid region. The increase in sediment connectivity and the reduction of soil moisture, among other deleterious consequences, endanger this desertification-prone region and reduce its capacity to support life and economic activities. Thus, we propose a model to simulate gully-erosion dynamics, which is derived from the existing physically based models of Foster and Lane (1983) and Sidorchuk (1999). The models were adapted so as to simulate long-term erosion. A threshold area shows the scale dependency of gully-erosion internal processes (bed scouring and wall erosion). To validate the model, we used three gullies that were over 6 decades old in an agricultural basin in the Brazilian state of Ceará. The geometry of the channels was assessed using an unmanned aerial vehicle and the structure from motion technique. Laboratory analyses were performed to obtain soil properties. Local and regional rainfall data were gauged to obtain sub-daily rainfall intensities. The threshold value (cross-section area of 2 m2) characterizes when erosion in the walls, due to loss of stability, becomes more significant than sediment detachment in the wet perimeter. The 30 min intensity can be used when no complete hydrographs from rainfall are available. Our model could satisfactorily simulate the gully-channel cross-section area growth over time, yielding a Nash–Sutcliffe efficiency of 0.85 and an R2 value of 0.94.

High-resolution spatial analysis of the variability in the subdaily rainfall time structure

The paper is motivated by the generally large variability in the rainfall time structure during the events with high subdaily precipitation totals in Czechia, which should be taken into account when designing water management systems to handle precipitation. The presented spatial analysis of the variability in the rainfall time structure is based on the regional frequency analysis of the 6-h totals with a spatial resolution of 1 km. The totals are quantified with the synergy method using the weather-radar-derived precipitation intensities and the totals measured by rain gauges. The frequency analysis employs the progressive and robust region-of-influence method. Instead of only one design storm hyetograph, each site is characterized by the possible occurrence of six hyetographs that are synthetized from the heavy rainfalls in Czechia. The percentage of individual hyetographs for a given design precipitation depth is assessed through the combination of the frequency analyses of the totals with respect to and regardless of the course of precipitation. The spatial heterogeneity in the percentages is significantly affected by the topography and amplifies with the increasing return period of the totals. The six synthetic hyetographs are represented relatively evenly in the lowlands. In contrast, the mountainous areas are characterized by a notable increase in the percentages of events of long-lasting steady rain, which are triggered by their circulation causes together with the significant role of the orographic precipitation enhancements, particularly through the windward orographic lifting, which is mainly on the northern and eastern slopes of the mountains. Future research on the dependence between the return period of the events of a specific precipitation course and the magnitude of the antecedent totals might be even more applicable to hydrological modeling in small catchments.

An advanced hidden Markov model for hourly rainfall time series

The hidden Markov framework is adapted to construct a compelling model for simulation of sub-daily rainfall, capable of capturing important characteristics of sub-daily rainfall well, including: long dry periods or droughts; seasonal and temporal variation in occurrence and intensity; and propensity for extreme values. These adaptations include both clone states and temporally non-homogeneous state persistence probabilities. Set in the Bayesian framework, a rich quantification of parametric and predictive uncertainty is available, and thorough model checking is made possible through posterior predictive analyses. Results from the model are highly interpretable, allowing for meaningful examination of diurnal, seasonal and annual variation in sub-daily rainfall occurrence and intensity. To demonstrate the effectiveness of this approach, both in terms of model fit and interpretability, the model is applied to an 8-year long time series of hourly observations.

The history of rainfall data time-resolution in a wide variety of geographical areas

Collected rainfall records by gauges lead to key forcings in most hydrological studies. Depending on sensor type and recording systems, such data are characterized by different time-resolutions (or temporal aggregations), ta. We present an historical analysis of the time-evolution of ta based on a large database of rain gauge networks operative in many study areas. Globally, ta data were collected for 25,423 rain gauge stations across 32 geographic areas, with larger contributions from Australia, USA, Italy and Spain. For very old networks early recordings were manual with coarse time-resolution, typically daily or sometimes monthly. With a few exceptions, mechanical recordings on paper rolls began in the first half of the 20th century, typically with ta of 1 h or 30 min. Digital registrations started only during the last three decades of the 20th century. This short period limits investigations that require long time-series of sub-daily rainfall data, e.g, analyses of the effects of climate change on short-duration (sub-hourly) heavy rainfall. In addition, in the areas with rainfall data characterized for many years by coarse time-resolutions, annual maximum rainfall depths of short duration can be potentially underestimated and their use would produce errors in the results of successive applications. Currently, only 50% of the stations provide useful data at any time-resolution, that practically means ta = 1 min. However, a significant reduction of these issues can be obtained through the information content of the present database. Finally, we suggest an integration of the database by including additional rain gauge networks to enhance its usefulness particularly in a comparative analysis of the effects of climate change on extreme rainfalls of short duration available in different locations.

The performance of the IMERG satellite-based product in identifying sub-daily rainfall events and their properties

Sub-daily rainfall information is essential for many hydrological applications, but ground-based data availability is still an issue in poorly gauged regions worldwide. Satellite remote sensing missions, such as the Global Precipitation Measurement (GPM) mission, have been playing a key role in estimating sub-daily rainfall data globally. However, the quality of such information needs to be carefully evaluated. Previous studies evaluating sub-daily data from the Integrated multi-satellitE Retrievals for GPM (IMERG) product considered only the rainfall depth over pre-defined periods (e.g., hourly or half-hourly), with no analysis of the ability and quality of the product in defining the actual rainfall events and the associated properties. Thus, the objective of this study is to evaluate the performance of the IMERG Final Run Version 06B (V06B) product in capturing sub-daily rainfall events and their properties (depth, duration and intensity) over Brazil. The analysis consisted of comparing the satellite estimates against the ground-based data from 1757 sub-daily rainfall gauges for a period of three years (2015–2017). This study used the minimum inter-event time (MIT) criterion to define independent rainfall events determined by dry periods: 1, 6 and 24 h. Results show that IMERG can properly estimate the sub-daily rainfall depth for the three MITs considered, with the best results found in the southern part of the country. This means that the IMERG product represents a good source of sub-daily rainfall depth data for hydrological and hydroclimatic applications in Brazil. On the other hand, the evaluation shows large overestimations and underestimations of the IMERG product for rainfall duration and intensity properties, respectively. The results obtained from this study provide a reference for IMERG users who require sub-daily rainfall data based on events and further knowledge about its properties.

Ability of an Australian reanalysis dataset to characterise sub-daily precipitation

Abstract. The high spatio-temporal variability of precipitation is often difficult to characterise due to limited measurements. The available low-resolution global reanalysis datasets inadequately represent the spatio-temporal variability of precipitation relevant to catchment hydrology. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) provides a high-resolution atmospheric reanalysis dataset across the Australasian region. For hydrometeorological applications, however, it is essential to properly evaluate the sub-daily precipitation from this reanalysis. In this regard, this paper evaluates the sub-daily precipitation from BARRA for a period of 6 years (2010–2015) over Australia against point observations and blended radar products. We utilise a range of existing and bespoke metrics for evaluation at point and spatial scales. We examine bias in quantile estimates and spatial displacement of sub-daily rainfall at a point scale. At a spatial scale, we use the fractions skill score as a spatial evaluation metric. The results show that the performance of BARRA precipitation depends on spatial location, with poorer performance in tropical relative to temperate regions. A possible spatial displacement during large rainfall is also found at point locations. This displacement, evaluated by comparing the distribution of rainfall within a day, could be quantified by considering the neighbourhood grids. On spatial evaluation, hourly precipitation from BARRA is found to be skilful at a spatial scale of less than 100 km (150 km) for a threshold of 75th percentile (90th percentile) at most of the locations. The performance across all the metrics improves significantly at time resolutions higher than 3 h. Our evaluations illustrate that the BARRA precipitation, despite discernible spatial displacements, serves as a useful dataset for Australia, especially at sub-daily resolutions. Users of BARRA are recommended to properly account for possible spatio-temporal displacement errors, especially for applications where the spatial and temporal characteristics of rainfall are deemed very important.

Global distribution of the intensity and frequency of hourly precipitation and their responses to ENSO

We investigate the global distribution of hourly precipitation and its connections with the El Niño–Southern Oscillation (ENSO) using both satellite precipitation estimates and the global sub-daily rainfall gauge dataset. Despite limited moisture availability over continental surfaces, we find that the highest mean and extreme hourly precipitation intensity (HPI) values are mainly located over continents rather than over oceans, a feature that is not evident in daily or coarser resolution data. After decomposing the total precipitation into the product of the number of wet hours (NWH) and HPI, we find that ENSO modulates total precipitation mainly through the NWH, while its effects on HPI are more limited. The contrasting responses to ENSO in NWH and HPI is particularly apparent at the rising branches of the Pacific and Atlantic Walker Circulations, and is also notable over land-based gauges in Australia, Malaysia, the USA, Japan and Europe across the whole distribution of hourly precipitation (i.e. extreme, moderate and light precipitation). These results provide new insights into the global precipitation distribution and its response to ENSO forcing.

Orographic Controls on Subdaily Rainfall Statistics and Flood Frequency in the Colorado Front Range, USA

AbstractGeneralizable relationships for how subdaily rainfall statistics imprint into runoff statistics are lacking. We use the Colorado Front Range, known for destructive rainfall‐triggered floods and landslides, to assess whether orographic patterns in runoff generation are a direct consequence of rainstorm climatology. Climatological analysis relies on a dense network of tipping‐bucket rain gauges and gridded precipitation frequency estimates from the National Oceanic and Atmospheric Administration to evaluate relationships among subdaily rainfall statistics, topography, and flood frequency throughout the South Platte River basin. We find that event‐scale rainfall statistics only weakly depend on elevation, suggesting that orographic gradients in runoff “extremes” are not simply a consequence of rainfall patterns. In contrast, bedrock exposure strongly varies with elevation in a way that plausibly explains enhanced runoff generation at lower elevations via reduced water storage capacity. These findings are suggestive of feedbacks between bedrock river evolution and hillslope hydrology not typically included in models of landscape evolution.

Using Mixed Probability Distribution Functions for Modelling Non-Zero Sub-Daily Rainfall in Australia

Probabilistic models for sub-daily rainfall predictions are important tools for understanding catchment hydrology and estimating essential rainfall inputs for agricultural and ecological studies. This research aimed at achieving theoretical probability distribution to non-zero, sub-daily rainfall using data from 1467 rain gauges across the Australian continent. A framework was developed for estimating rainfall data at ungauged locations using the fitted model parameters from neighbouring gauges. The Lognormal, Gamma and Weibull distributions, as well as their mixed distributions were fitted to non-zero six-minutes rainfall data. The root mean square error was used to evaluate the goodness of fit for each of these distributions. To generate data at ungauged locations, parameters of well-fit models were interpolated from the four closest neighbours using inverse weighting distance method. Results show that the Gamma and Weibull distributions underestimate and lognormal distributions overestimate the high rainfall events. In general, a mixed model of two distributions was found better compared to the results of an individual model. Among the five models studied, the mixed Gamma and Lognormal (G-L) distribution produced the minimum root mean square error. The G-L model produced the best match to observed data for high rainfall events (e.g., 90th, 95th, 99th, 99.9th and 99.99th percentiles).

A regional similarity-based approach for sub-daily rainfall nonparametric generation

ABSTRACT Rainfall time series with high temporal resolution are required for estimating storm events for the design of urban drainage systems, for performing rainfall-runoff simulation in small catchments and for modeling flash-floods. Nonetheless, large and continuous sub-daily rainfall samples are often unavailable. For dealing with the limited availability of high-resolution rainfall records, in both time and space, this paper explored an alternative version of the k-nearest neighbors algorithm, coupled with the method of fragments (KNN-MOF model), which utilizes a state-based logic for simulating consecutive wet days and a regionalized similarity-based approach for sampling fragments from hydrologically similar nearby stations. The proposed disaggregation method was applied to 40 rainfall gauging stations located in the São Francisco and Doce river catchments. Disaggregation of daily rainfall was performed for the durations of 60, 180 and 360 minutes. Results indicated the model presented an appropriate performance to disaggregate daily rainfall, reasonably reproducing sub-daily summary statistics. In addition, the annual block-maxima behavior, even for low exceedance probabilities, was relatively well described, although not all expected variability in the quantiles was properly summarized by the model. Overall, the proposed approach proved a sound and easy to implement alternative for simulating continuous sub-daily rainfall amounts from coarse-resolution records.

Sub-Daily Rainfall Intensity Extremes: Evaluating Suitable Indices at Australian Arid and Wet Tropical Observing Sites

Rainfall intensity extremes are relevant to many aspects of climatology, climate change, and landsurface processes. Intensity is described and analysed using a diversity of approaches, reflecting its importance in these diverse areas. The characteristics of short-interval intensity extremes, such as the maximum 5-min intensity, are explored here. It is shown that such indices may have marked diurnal cycles, as well as seasonal variability. Some indices of intensity, such as the SDII (simple daily intensity index), provide too little information for application to landsurface processes. Upper percentiles of the intensity distribution, such as the 95th and 99th percentiles (Q95 and Q99) are used as indices of extreme intensity, but problematically are affected by changes in intensity below the nominated threshold, as well as above it, making the detection of secular change, and application to sites with contrasting rainfall character, challenging. For application to landsurface processes, a new index is introduced. This index (RQ95), is that intensity or rainfall rate above which 5% of the total rainfall is delivered. This index better reflects intense rainfall than does Q95 of even 5-min accumulation duration (AD) rainfall depths. Such an index is helpful for detecting secular change at an observing station, but, like Q95, remains susceptible to the effects of change elsewhere in the distribution of intensities. For understanding impacts of climate and climate change on landsurface processes, it is argued that more inclusive indices of intensity are required, including fixed intensity criteria.

Observed Link of Extreme Hourly Precipitation Changes to Urbanization over Coastal South China

AbstractUnderstanding changes in subdaily rainfall extremes is critical to urban planners for building more sustainable and resilient cities. In this study, the hourly precipitation data in 1971–2016 from 61 rain gauges are combined with historical land-use change data to investigate changes in extreme hourly precipitation (EXHP) in the Pearl River delta (PRD) region of South China. Also, 120 extreme rainfall events (EXREs) during 2011–16 are analyzed using observations collected at densely distributed automatic weather stations and radar network. Statistically significant increase of hourly precipitation intensity leads to higher annual amounts of both total and extreme precipitation over the PRD urban cluster in the rapid urbanization period (about 1994–2016) than during the preurbanization era (1971 to about 1993), suggesting a possible link between the enhanced rainfall and the rapid urbanization. Those urbanization-related positive trends are closely related to more frequent occurrence of abrupt rainfall events with short duration (≤6 h) than the continuous or growing rainfall events with longer duration. The 120 EXREs in 2011–16 are categorized into six types according to the originating location and movement of the extreme-rain-producing storms. Despite the wide range of synoptic backgrounds and seasons, rainfall intensification by the strong urban heat island (UHI) effect is a clear signal in all the six types, especially over the inland urban cluster with prominent UHIs. The UHI thermal perturbation probably plays an important role in the convective initiation and intensification of the locally developed extreme-rain-producing storms during the daytime.

Extreme rainfall in Mediterranean France during the fall: added value of the CNRM-AROME Convection-Permitting Regional Climate Model

South-East France is a region often affected by heavy precipitating events the characteristics of which are likely to be significantly impacted in the future climate. In this study, cnrm-arome, a Convection-Permitting Regional Climate Model with a 2.5 km horizontal resolution is compared to its forcing model, the Regional Climate Model aladin-climate at a horizontal resolution of 12.5 km, self-driven by the era-interim reanalysis. An hourly observation dataset with a resolution of 1 km, comephore, is used in order to assess simulated surface precipitation from a seasonal to hourly scale. The representation of the spatial pattern of fall precipitation climatology is improved by cnrm-arome. It also shows a clear added value with respect to aladin-climate through the improvement of the localization and intensity of extreme rainfall on a daily and hourly time scale on both fine and coarse spatial scales (2.5, 12.5 and 50 km). cnrm-arome in particular is able to simulate intense rainfall on lowlands and makes sub-daily rainfall events more intense than aladin-climate. cnrm-arome still underestimates very extreme precipitation from above 30 mm/h or 230 mm/day.

GSDR: A Global Sub-Daily Rainfall Dataset

Abstract Extreme short-duration rainfall can cause devastating flooding that puts lives, infrastructure, and natural ecosystems at risk. It is therefore essential to understand how this type of extreme rainfall will change in a warmer world. A significant barrier to answering this question is the lack of sub-daily rainfall data available at the global scale. To this end, a global sub-daily rainfall dataset based on gauged observations has been collated. The dataset is highly variable in its spatial coverage, record length, completeness and, in its raw form, quality. This presents significant difficulties for many types of analyses. The dataset currently comprises 23 687 gauges with an average record length of 13 years. Apart from a few exceptions, the earliest records begin in the 1950s. The Global Sub-Daily Rainfall Dataset (GSDR) has wide applications, including improving our understanding of the nature and drivers of sub-daily rainfall extremes, improving and validating of high-resolution climate models, and developing a high-resolution gridded sub-daily rainfall dataset of indices.

Projected intensification of sub-daily and daily rainfall extremes in convection-permitting climate model simulations over North America: implications for future intensity–duration–frequency curves

Abstract. Convection-permitting climate models have been recommended for use in projecting future changes in local-scale, short-duration rainfall extremes that are of the greatest relevance to engineering and infrastructure design, e.g., as commonly summarized in intensity–duration–frequency (IDF) curves. Based on thermodynamic arguments, it is expected that rainfall extremes will become more intense in the future. Recent evidence also suggests that shorter-duration extremes may intensify more than longer durations and that changes may depend on event rarity. Based on these general trends, will IDF curves shift upward and steepen under global warming? Will long-return-period extremes experience greater intensification than more common events? Projected changes in IDF curve characteristics are assessed based on sub-daily and daily outputs from historical and late 21st century pseudo-global-warming convection-permitting climate model simulations over North America. To make more efficient use of the short model integrations, a parsimonious generalized extreme value simple scaling (GEVSS) model is used to estimate historical and future IDF curves (1 to 24 h durations). Simulated historical sub-daily rainfall extremes are first evaluated against in situ observations and compared with two high-resolution observationally constrained gridded products. The climate model performs well, matching or exceeding performance of the gridded datasets. Next, inferences about future changes in GEVSS parameters are made using a Bayesian false discovery rate approach. Large portions of the domain experience significant increases in GEVSS location (>99 % of grid points), scale (>88 %), and scaling exponent (>39 %) parameters, whereas almost no significant decreases are projected to occur (<1 %, <5 %, and <5 % respectively). The result is that IDF curves tend to shift upward (increases in location and scale), and, with the exception of the eastern US, steepen (increases in scaling exponent), which leads to the largest increases in return levels for short-duration extremes. The projected increase in the GEVSS scaling exponent calls into question stationarity assumptions that form the basis for existing IDF curve projections that rely exclusively on simulations at the daily timescale. When changes in return levels are scaled according to local temperature change, median scaling rates, e.g., for the 10-year return level, are consistent with the Clausius–Clapeyron (CC) relation at 1 to 6 h durations, with sub-CC scaling at longer durations and modest super-CC scaling at sub-hourly durations. Further, spatially coherent but small increases in dispersion – the ratio of scale and location parameters – of the GEVSS distribution are found over more than half of the domain, providing some evidence for return period dependence of future changes in extreme rainfall.