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Local, short-duration extreme precipitation events can cause floodings and have massive economic consequences. The climate change impact on such events is of great interest, but due to the small spatio-temporal scales involved, these are challenging to properly represent in climate models. This study analyses a new sub-kilometre (750?m) HARMONIE-Climate model simulation driven by ERA5 reanalysis data. Three convection-permitting models at 750?m, 3 and 5?km grid distance are analysed and compared with driving reanalyses, intermediate model simulations and a dense rain gauge network. The representation of convective events is analysed by a range of metrics categorised as spatial, temporal and event-focused. Precipitation events are analysed at both hourly and sub-hourly scales and a clear difference between model performance on these scales is found. Overall, we find a better performance for HCLIM750m for most metrics, yet the added benefits of the computationally intensive sub-kilometre scale simulation seems limited compared to the convection-permitting models at 3 and 5?km.

High-resolution precipitation observation based on signal attenuation in a Commercial Microwave Link (CML) network is an emerging technique that is becoming more and more used. Commonly, the raw data – line measurements from successive time steps – are mapped onto a grid to estimate precipitation fields with a full spatio-temporal coverage. Assuming the CML-estimated precipitation to be accurate, the attainable resolutions in time and space are primarily dependent on two factors: (i) the spatial distribution of the link network and (ii) the spatial correlation properties of the precipitation. Here we outline a pragmatic method for estimating the optimal resolution based on variogram analysis. The method is demonstrated using a CML network and a representative variogram in Stockholm, Sweden. Conceivable applications include preliminary investigations in cities considering starting CML-based precipitation observations.

Study regionThe Nordic-Baltic region has experienced numerous flooding episodes resulting from heavy rainfall. Such events are costly and may potentially threaten the safety of the population. In this paper we present a temporally long and spatially dense dataset of annual maximum daily precipitation and their date of occurrence measured in a large region covering Fennoscandia and the Baltics (Dyrrdal et al., 2021, doi:10.11582/2021.00015).Study focusWe analyse the long-term (1901–2020) changes at 138 stations and short-term (1969–2020) changes at 724 stations for both annual maxima and their date of occurrence. Further, we assess the climatology of heavy precipitation including record evens, as well as changes in design values.New hydrological insight for the regionResults show a majority of positive trends in daily annual maxima and the 5-year return level, with hotspots in southeast of Norway, southern Sweden and southwest of Finland. Generally, annual maximum precipitation events occur somewhat later in the year now compared to the beginning of the last century. The 5-year return level is relatively homogeneous across the Nordic-Baltic region, with values between 30 and 50 mm, except for a few lower values in Finland and high values mainly exceeding 70 mm at the west coast of Norway.

Regional climate model simulations have routinely been applied to assess changes in precipitation extremes at daily time steps. However, shorter sub-daily extremes have not received as much attention. This is likely because of the limited availability of high temporal resolution data, both for observations and for model outputs. Here, summertime depth duration frequencies of a subset of the EURO-CORDEX 0.11∘ ensemble are evaluated with observations for several European countries for durations of 1 to 12 h. Most of the model simulations strongly underestimate 10-year depths for durations up to a few hours but perform better at longer durations. The spatial patterns over Germany are reproduced at least partly at a 12 h duration, but all models fail at shorter durations. Projected changes are assessed by relating relative depth changes to mean temperature changes. A strong relationship with temperature is found across different subregions of Europe, emission scenarios and future time periods. However, the scaling varies considerably between different combinations of global and regional climate models, with a spread in scaling of around 1–10 % K−1 at a 12 h duration and generally higher values at shorter durations.

We investigate short-duration rainfall extremes in a 22-year long (1996–2017) time series of 15-min values from 128 stations in Sweden. Two different approaches are used to fit extreme value distributions (EVDs) to the data: generalized extreme value distribution with annual maximum values and generalized Pareto distribution with peak-over-threshold values. First, EVDs are fitted to the data from each station, and the extreme value parameters are used together with the station coordinates in a cluster analysis. Based on the clustering results, a division of Sweden into four regions is proposed. Following the station-year method, the time series from all stations in each cluster are pooled into regional series after which depth–duration–frequency (DDF) statistics are calculated. Averaged over durations and return periods, the highest DDF values are found in region south-west, being ∼25% higher than the lowest ones found in the northern region. A six-parameter DDF function is proposed which, together with a simple empirical expression for the associated confidence interval, allows for easy DDF estimation at arbitrary durations and return periods, including uncertainty. No tendencies of change in magnitude or frequency of the short-duration extremes are found during the period.

Extreme precipitation events have a major impact upon our society. Although many studies have indicated that it is likely that the frequency of such events will increase in a warmer climate, little has been done to assess changes in extreme precipitation at a sub-daily scale. Recently, there is more and more evidence that high-resolution convection-permitting models (CPMs) (grid-mesh typically < 4 km) can represent especially short-duration precipitation extremes more accurately when compared with coarser-resolution regional climate models (RCMs).This study investigates sub-daily and daily precipitation characteristics based on hourly output data from the HARMONIE-Climate model at 3-km and 12-km grid-mesh resolution over the Nordic region between 1998 and 2018. The RCM modelling chain uses the ERA-Interim reanalysis to drive a 12-km grid-mesh simulation which is further downscaled to 3-km grid-mesh resolution using a non-hydrostatic model set-up.The statistical properties of the modeled extreme precipitation are compared to several sub-daily and daily observational products, including gridded and in-situ gauge data, from April to September. We investigate the skill of the model to represent different aspects of the frequency and intensity of extreme precipitation as well as intensity–duration–frequency (IDF) curves that are commonly used to investigate short duration extremes from an urban planning perspective. The high grid resolution combined with the 20-year-long simulation period allows for a robust assessment at a climatological time scale and enables us to examine the added value of high-resolution CPM in reproducing precipitation extremes over the Nordic region. Based on the tentative results, the high-resolution CPM can realistically capture the characteristics of precipitation extremes, for instance, in terms of improved diurnal cycle and maximum intensities of sub-daily precipitation.

Regional climate model simulations have routinely been applied to assess changes in precipitation extremes at daily time steps. However, shorter sub-daily extremes have not received as much attention. This is likely because of the limited availability of high temporal resolution data, both for observations and for model outputs. Here, summertime depth duration frequencies of a sub-set of the EURO-CORDEX 0.11° ensemble is evaluated with observations for several European countries for durations of one to 12 h. Most of the model simulations strongly underestimate 10-year depths for durations up to a few hours, but do better on 12 h durations. All models fail in reproducing observed spatial patterns over Germany for durations shorter than 12 h, but all reproduce the pattern at least partly at 12 h duration. Large-scale driven spatial patterns, such as the extreme depths in southern France are better captured also at shorter durations, albeit severely underestimated. Projected changes are assessed by relating relative depth changes to mean temperature changes. A strong relationship with temperature is found across sub-regions of Europe, the emission scenario and future time period. However, there is an equally strong dependency on the global and regional model applied, with a spread in scaling of around 1–10 %/K at 12 h duration, and generally higher values at shorter durations.

Hydropower is a major energy source in Sweden, and proper reservoir management prior to the spring-flood onset is crucial for optimal production. This requires accurate forecasts of the accumulated discharge in the spring-flood period (i.e. the spring-flood volume, SFV). Today's SFV forecasts are generated using a model-based climatological ensemble approach, where time series of precipitation and temperature from historical years are used to force a calibrated and initialized set-up of the HBV model. In this study, a number of new approaches to spring-flood forecasting that reflect the latest developments with respect to analysis and modelling on seasonal timescales are presented and evaluated. Three main approaches, represented by specific methods, are evaluated in SFV hindcasts for the Swedish river Vindelälven over a 10-year period with lead times between 0 and 4 months. In the first approach, historically analogue years with respect to the climate in the period preceding the spring flood are identified and used to compose a reduced ensemble. In the second, seasonal meteorological ensemble forecasts are used to drive the HBV model over the spring-flood period. In the third approach, statistical relationships between SFV and the large-sale atmospheric circulation are used to build forecast models. None of the new approaches consistently outperform the climatological ensemble approach, but for early forecasts improvements of up to 25 % are found. This potential is reasonably well realized in a multi-method system, which over all forecast dates reduced the error in SFV by ∼ 4 %. This improvement is limited but potentially significant for e.g. energy trading.

The present paper develops linear regression models based on singular value decomposition (SVD) with the aim of downscaling atmospheric variables statistically to estimate average rainfall in the Chikugo River Basin, Kyushu Island, southern Japan, on a 12-hour basis. Models were designed to take only significantly correlated areas into account in the downscaling procedure. By using particularly precipitable water in combination with wind speeds at 850 hPa, correlation coefficients between observed and estimated precipitation exceeding 0.8 were reached. The correlations exhibited a seasonal variation with higher values during autumn and winter than during spring and summer. The SVD analysis preceding the model development highlighted three important features of the rainfall regime in southern Japan: (1) the so-called Bai-u front which is responsible for the majority of summer rainfall, (2) the strong circulation pattern associated with autumn rainfall, and (3) the strong influence of orographic lifting creating a pronounced east-west gradient across Kyushu Island. Results confirm the feasibility of establishing meaningful statistical relationships between atmospheric state and basin rainfall even at time scales of less than one day. Keywords: atmospheric downscaling, precipitation, rainfall, singular value decomposition, southern Japan

As the risk of a forest fire is largely influenced by weather, evaluating its tendency under a changing climate becomes important for management and decision making. Currently, biases in climate models make it difficult to realistically estimate the future climate and consequent impact on fire risk. A distribution-based scaling (DBS) approach was developed as a post-processing tool that intends to correct systematic biases in climate modelling outputs. In this study, we used two projections, one driven by historical reanalysis (ERA40) and one from a global climate model (ECHAM5) for future projection, both having been dynamically downscaled by a regional climate model (RCA3). The effects of the post-processing tool on relative humidity and wind speed were studied in addition to the primary variables precipitation and temperature. Finally, the Canadian Fire Weather Index system was used to evaluate the influence of changing meteorological conditions on the moisture content in fuel layers and the fire-spread risk. The forest fire risk results using DBS are proven to better reflect risk using observations than that using raw climate outputs. For future periods, southern Sweden is likely to have a higher fire risk than today, whereas northern Sweden will have a lower risk of forest fire.

Many hydrological hazards are closely connected to local precipitation (extremes), especially in small and urban catchments. The use of regional climate model (RCM) data for small-scale hydrological climate change impact assessment has long been nearly unfeasible because of the low spatial resolution. The RCM resolution is, however, rapidly increasing, approaching the size of small catchments and thus potentially increasing the applicability of RCM data for this purpose. The objective of this study is to explore to what degree subhourly temporal precipitation statistics in an RCM converge to observed point statistics when gradually increasing the resolution from 50 to 6 km. This study uses precipitation simulated by RCA3 at seven locations in southern Sweden during 1995–2008. A positive impact of higher resolution was most clearly manifested in 10-yr intensity–duration–frequency (IDF) curves. At 50 km the intensities are underestimated by 50%–90%, but at 6 km they are nearly unbiased, when averaged over all locations and durations. Thus, at 6 km, RCA3 apparently generates low-frequency subdaily extremes that resemble the values found in point observations. Also, the reproduction of short-term variability and less extreme maxima were overall improved with increasing resolution. For monthly totals, a slightly increased overestimation with increasing resolution was found. The bias in terms of wet fraction and wet spell characteristics was overall not strongly dependent on resolution. These metrics are, however, influenced by the cutoff threshold used to separate between wet and dry time steps as well as the wet spell definition.

Our current knowledge of the character of rainfall events in Sweden associated with extreme short-term accumulations and their predictability by forecasting, is very limited. In this study, observations from automatic stations and weather radars in Sweden were analysed to identify and characterise extreme short-term events. Often shorter-duration (1–6 h) extreme events were associated with small-scale structures, dominated by single cells, and longer-duration (12–24 h) events with less variable, larger-scale fields. For lead time 3 h, ∼20% of the events were forecasted at the correct place with an error of

Climate change is expected to generate higher short-term precipitation intensities, which may have negative consequences in terms of, for example, increased risk of flooding and sewer overflow. In this study, extreme precipitation for durations between 30 min and 1 day in simulations with the RCA3 regional climate model (RCM) for Sweden are analysed. As compared with daily observations in the period 1961–2010, the simulated extremes are found to be overall realistic with respect to magnitude, spatial homogeneity and temporal variability. In the ensemble of future projections, from 1981 to 2010 the 10-year 30-min precipitation will increase by 6% until 2011–2040, 15% until 2041–2070 and 23% until 2071–2100. The increase decreases with increasing duration and at the daily scale the percentage values are approximately halved. The values are largely consistent with earlier estimates. Assessment of the impacts on the results of the spatial resolution and the specific RCM used indicated possibilities of both smaller and larger future increases.

Hydrological climate change impact assessment is generally performed by following a sequence of steps from global and regional climate modelling, through data tailoring (bias-adjustment and downscaling) and hydrological modelling, to analysis and impact assessment. This “climate-hydrology-assessment chain” has been developed with a primary focus on applicability to a medium-sized rural basin, which has been and still is the main type of domain investigated in this context. However, impact assessment is to an increasing degree being performed at scales smaller or larger than the medium-sized rural basin. Small-scale assessment includes e.g., impacts on solute transport and urban hydrology and large-scale assessment includes e.g., climate teleconnections and continental modelling. In both cases, additional complexity is introduced in the process and additional demands are placed on all components involved, i.e., climate and hydrology models, tailoring methods, assessment principles, and tools. In this paper we provide an overview of recent progress with respect to small- and large-scale hydrological climate change impact assessment. In addition, we wish to highlight some key issues that emerged as a consequence of the scale and that need further attention from now on. While we mainly use examples from work performed in Europe for illustration, the progress generally reflects the overall state of the art and the issues considered are of a generic character.

As climate change could have considerable influence on hydrology and corresponding water management, appropriate climate change inputs should be used for assessing future impacts. Although the performance of regional climate models (RCMs) has improved over time, systematic model biases still constrain the direct use of RCM output for hydrological impact studies. To address this, a distribution-based scaling (DBS) approach was developed that adjusts precipitation and temperature from RCMs to better reflect observations. Statistical properties, such as daily mean, standard deviation, distribution and frequency of precipitation days, were much improved for control periods compared to direct RCM output. DBS-adjusted precipitation and temperature from two IPCC Special Report on Emissions Scenarios (SRESA1B) transient climate projections were used as inputs to the HBV hydrological model for several river basins in Sweden for the period 1961–2100. Hydrological results using DBS were compared to results with the widely-used delta change (DC) approach for impact studies. The general signal of a warmer and wetter climate was obtained using both approaches, but use of DBS identified differences between the two projections that were not seen with DC. The DBS approach is thought to better preserve the future variability produced by the RCM, improving usability for climate change impact studies.

There is growing interest in the impact of climate change on urban hydrological processes. Such assessment may be based on the precipitation output from climate models. To date, the model resolution in both time and space has been too low for proper assessment, but at least in time the resolution of available model output is approaching urban scales. In this paper, 30-min precipitation from a model grid box covering Kalmar City, Sweden, is compared with high-resolution (tipping-bucket) observations from a gauge in Kalmar. The model is found to overestimate the frequency of low rainfall intensities, and therefore the total volume, but reasonably well reproduce the highest intensities. Adapting climate model data to urban drainage applications can be done in several ways but a popular way is the so-called Delta Change (DC) method. In this method, relative changes in rainfall characteristics estimated from climate model output are transferred to an observed rainfall time series, generally by multiplicative factors. In this paper, a version of the method is proposed in which these DC factors (DCFs) are related to the rainfall intensity level. This is achieved by calculating changes in the probability distribution of rainfall intensities and modelling the DCFs as a function of percentile. Applying this method in Kalmar indicated that in summer and autumn, high intensities will increase by 20–60% by year 2100, whereas low intensities remain stable or decrease. In winter and spring, generally all intensity levels increase similarly. The results were transferred to the observed time series by varying the volume of the tipping bucket to reflect the estimated intensity changes on a 30-min time scale. In an evaluation of the transformed data at a higher 5-min resolution, effects on the intensity distribution as well as single precipitation events were demonstrated. In particular, qualitatively different changes in peak intensity and total volume are attainable, which is required in light of expected future changes of the precipitation process and a step forward as compared with simpler DC approaches. Using the DC transformed data as input in urban drainage simulations for a catchment in Kalmar indicated an increase of the number of surface floods by 20–45% during this century.

A system for ensemble streamflow prediction, ESP, has been operational at SMHI since July 2004, based on 50 meteorological ensemble forecasts from ECMWF. Hydrological ensemble forecasts are produced daily for 51 basins in Sweden. All ensemble members, as well as statistics (minimum, 25% quartile, median, 75% quartile and maximum), are stored in a database. This paper presents an evaluation of the first 18 months of ESP median forecasts from this system, and in particular their performance in comparison with today's categorical forecast. The evaluation was made in terms of three statistical measures: bias B, root mean square error RMSE and absolute peak flow error PE. For ESP forecasts the bias ranged between -20% and 80% with a systematic overestimation for Sweden as a whole. A comparison between bias in input precipitation and ESP output, respectively, revealed only a weak relationship, but streamflow overestimation is likely related mainly to model properties. The results from the streamflow forecast comparison showed that the ESP median in deterministic terms performs overall as well as the presently used categorical forecast. Further, ESP has the advantage of providing at least a qualitative measure of the uncertainty in the forecasts, with probability forecasts being the ultimate goal.

The propagation of spatio-temporal errors in precipitation estimates to runoff errors in the output from the conceptual hydrological HBV model was investigated. The study region was the Gimån catchment in central Sweden, and the period year 2002. Five precipitation sources were considered: NWP model (H22), weather radar (RAD), precipitation gauges (PTH), and two versions of a mesoscale analysis system (M11, M22). To define the baseline estimates of precipitation and runoff, used to define seasonal precipitation and runoff biases, the mesoscale climate analysis M11 was used. The main precipitation biases were a systematic overestimation of precipitation by H22, in particular during winter and early spring, and a pronounced local overestimation by RAD during autumn, in the western part of the catchment. These overestimations in some cases exceeded 50% in terms of seasonal subcatchment relative accumulated volume bias, but generally the bias was within ±20%. The precipitation data from the different sources were used to drive the HBV model, set up and calibrated for two stations in Gimån, both for continuous simulation during 2002 and for forecasting of the spring flood peak. In summer, autumn and winter all sources agreed well. In spring H22 overestimated the accumulated runoff volume by ~50% and peak discharge by almost 100%, owing to both overestimated snow depth and precipitation during the spring flood. PTH overestimated spring runoff volumes by ~15% owing to overestimated winter precipitation. The results demonstrate how biases in precipitation estimates may exhibit a substantial space-time variability, and may further become either magnified or reduced when applied for hydrological purposes, depending on both temporal and spatial variations in the catchment. Thus, the uncertainty in precipitation estimates should preferably be specified as a function of both time and space.

Several studies have used artificial neural networks (NNs) to estimate local or regional precipitation/rainfall on the basis of relationships with coarse-resolution atmospheric variables. None of these experiments satisfactorily reproduced temporal intermittency and variability in rainfall. We attempt to improve performance by using two approaches: (1) couple two NNs in series, the first to determine rainfall occurrence, and the second to determine rainfall intensity during rainy periods; and (2) categorize rainfall into intensity categories and train the NN to reproduce these rather than the actual intensities. The experiments focused on estimating 12-h mean rainfall in the Chikugo River basin, Kyushu Island, southern Japan, from large-scale values of wind speeds at 850 hPa and precipitable water. The results indicated that (1) two NNs in series may greatly improve the reproduction of intermittency; (2) longer data series are required to reproduce variability; (3) intensity categorization may be useful for probabilistic forecasting; and (4) overall performance in this region is better during winter and spring than during summer and autumn.

A review is made of current methods for assessing future changes in urban rainfall extremes and their effects on urban drainage systems, due to anthropogenic-induced climate change. The review concludes that in spite of significant advances there are still many limitations in our understanding of how to describe precipitation patterns in a changing climate in order to design and operate urban drainage infrastructure. Climate change may well be the driver that ensures that changes in urban drainage paradigms are identified and suitable solutions implemented. Design and optimization of urban drainage infrastructure considering climate change impacts and co-optimizing these with other objectives will become ever more important to keep our cities habitable into the future. Refereed/Peer-reviewed

RainPortal (http://sudplan.ait.ac.at/RainPortalServer) is a web portal that has three main targets: 1) Provide access to scientifically sound data related to some key climate change aspects in Europe, 2) Provide concrete data about climate change related local precipitation changes by using climate model output and 3) Easy usage and incorporation of the provided information into existing planning workflows.

Cities are becoming increasingly vulnerable to flooding because of rapid urbanization, installation of complex infrastructure, and changes in the precipitation patterns caused by anthropogenic climate change. The present paper provides a critical review of the current state-of-the-art methods for assessing the impacts of climate change on precipitation at the urban catchment scale. Downscaling of results from global circulation models or regional climate models to urban catchment scales are needed because these models are not able to describe accurately the rainfall process at suitable high temporal and spatial resolution for urban drainage studies. The downscaled rainfall results are however highly uncertain, depending on the models and downscaling methods considered. This uncertainty becomes more challenging for rainfall extremes since the properties of these extremes do not automatically reflect those of average precipitation. In this paper, following an overview of some recent advances in the development of innovative methods for assessing the impacts of climate change on urban rainfall extremes as well as on urban hydrology and hydraulics, several existing difficulties and remaining challenges in dealing with this assessment are discussed and further research needs are described.

Part 6: Climate Services and Environmental Tools for Urban Planning and Climate Change Applications and Services; International audience; A future increase of short-term precipitation intensities may lead to problems in sewer systems, such as increased overflow volumes and flood risks. To quantify the consequences, downscaling of climate model precipitation is required to the scales relevant in urban hydrology. In the SUDPLAN project, a system where users may upload historical time series to be used as a basis for such downscaling is being developed. In this paper, the method (Delta Change) is outlined along with brief descriptions of the technical solution and result visualization.

Lake Vanern and River Gota alv in southern Sweden constitute a large and complex hydrological system that is highly vulnerable to climate change. In this study, an ensemble of 12 regional climate projections is used to simulate the inflow to Lake Vanern by the HBV hydrological model. By using distribution based scaling of the climate model output, all projections can accurately reproduce the annual cycle of mean monthly inflows for the period 1961–1990 as simulated using HBV with observed temperature and precipitation (‘HBVobs’). Significant changes towards higher winter inflow and a reduced spring flood were found when comparing the period 1991–2008 to 1961–1990 in the HBVobs simulations and the ability of the regional projections to reproduce these changes varied. The main uncertainties in the projections for 1991–2008 were found to originate from the global climate model used, including its initialization, and in one case, the emissions scenario, whereas the regional climate model used and its resolution showed a smaller influence. The projections that most accurately reproduce the recent change suggest that the current trends in the winter and spring inflows will continue over the period 2009–2030.