Daily Precipitation Time Series Research Articles

A probabilistic-deterministic approach for assessing climate change effects on infection risks downstream of sewage emissions from CSOs

The discharge of pathogens into urban recreational water bodies during combined sewer overflows (CSOs) pose a potential threat for public health which may increase in the future due to climate change. Improved methods are needed for predicting the impact of these effects on the microbiological urban river water quality and infection risks during recreational use. The aim of this study was to develop a novel probabilistic-deterministic modelling approach for this purpose building on physically plausible generated future rainfall time series. The approach consists of disaggregation and validation of daily precipitation time series from 21 regional climate models for a reference period (1971–2000, C20), a near-term future period (2021–2050, NTF) and a long-term future period (2071–2100, LTF) into sub-daily scale, and predicting the concentrations of enterococci and Giardia and Cryptosporidium, and infection risks during recreational use in the river downstream of the sewage emissions from CSOs. The approach was tested for an urban river catchment in Austria which is used for recreational activities (i.e. swimming, playing, wading, hand-to-mouth contact). According to a worst-case scenario (i.e. children bathing in the river), the 95th percentile infection risks for Giardia and Cryptosporidium range from 0.08 % in winter to 8 % per person and exposure event in summer for C20. The infection risk increase in the future is up to 0.8 log10 for individual scenarios. The results imply that measures to prevent CSOs may be needed to ensure sustainable water safety. The approach is promising for predicting the effect of climate change on urban water safety requirements and for supporting the selection of sustainable mitigation measures. Future studies should focus on reducing the uncertainty of the predictions at local scale.

Continental‐scale trends of daily precipitation records in late 20th century decades and 21st century projections: An analysis of observations, reanalyses and CORDEX‐CORE projections

AbstractWe apply a methodology to identify and count records (events of unprecedented intensity) in daily precipitation time series to two sets of data: (1) different observational and reanalysis products for recent decades and (2) twenty‐first century projections (RCP8.5 and RCP2.6 scenarios) completed with two regional climate models driven by three global climate models over nine continental‐scale domains. Comparison of the detected (or actual) number of records with the corresponding number theoretically expected in stationary climate conditions (or “reference” number of records) provides indications of trends in daily precipitation extremes, as expected in a changing climate. In particular, we measure deviations from stationary conditions using the ratio of actual to reference records (RAtR) as a basic metric. We find that the observational products provide mixed indications of precipitation record trends across regions, while in the reanalysis products and the model simulations for the historical period the RAtR value shows a prevailing increasing trend with time over most continents. The RAtR shows a consistent and pronounced increase in all RCP8.5 continental‐scale projections, when sustained warming occurs throughout the 21st century, while smaller to no significant trends are found in the RCP2.6 scenario, when the warming stabilizes after about mid‐21st century. These results are indicative of an increase in precipitation extremes with global warming as measured by the higher number of local precipitation events of unprecedented intensity compared to what expected in stationary climate conditions, although a marked variability of this response is found across different regions. Our method can have useful applications in detection and attribution of hydroclimatic extremes and in impact and vulnerability assessment studies.

On the Examination of the Relationship between Mean and Extreme Precipitation and Circulation Types over Southern Romania

The main goal of the present study is to identify the prevailing atmospheric circulation patterns (circulation types) that are associated with the occurrence of precipitation (both mean and extreme) over southern Romania. A daily circulation type calendar derived from an automatic and objective classification scheme is used in synergy with the daily precipitation time series from five weather stations in the study area for a sixty-year period (1961–2020). Both mean and extreme precipitation do not show statistically significant trends, except for the annual precipitation at Constanța, for the value with daily precipitation totals greater than the 95th percentile at Craiova and the number of days exceeding the 99th percentile at Buzău and Râmnicu -Vâlcea, where significant negative trends were noticed. Moreover, the precipitation trends were analyzed in relation to the atmospheric circulation types. Non-significant positive trends were observed for the precipitation amounts (annually, winter, spring, and autumn) corresponding to very rainy circulation types (C, Cwsw), while for summer, the equivalent trends were negative. Moreover, it became evident that during extreme precipitation events, the predominant circulation types (C, Cwsw) are associated with western or almost western atmospheric circulation and Mediterranean- or Atlantic-originated depressions.

Optimal Probability Distribution and Applicable Minimum Time-Scale for Daily Standardized Precipitation Index Time Series in South Korea

The Standardized Precipitation Index (SPI) is a standardized measure of the variability of precipitation and is widely used for drought assessment around the world. In general, the probability distribution used to calculate the SPI in many studies is Gamma. In addition, a monthly time-scale is applied to calculate the SPI to assess drought based on atmospheric moisture supply over the medium-to-long term. However, probability distributions other than Gamma are applied in various regions, and the need for a daily time-scale is emerging as concerns about fresh drought increase. There are two main innovations of our work. The first is that we investigate the optimal probability distribution of daily SPIs rather than monthly SPIs, and the second is that we address the issue of determining the minimum time-scale that can be applied when applying a daily time-scale. In this study, we investigate the optimal probability distribution and the minimum-applicable time-scale for calculating the daily SPI using daily precipitation time series observed over 42 years at 56 sites in South Korea. Six candidate probability distributions (Gumbel, Gamma, GEV, Log-logistic, Log-normal, and Weibull) and ten time-scales (5 day, 10 day, 15 day, 21 day, 30 day, 60 day, 90 day, 180 day, 270 day, and 365 day) were applied to calculate the daily SPI. A chi-square test and AIC were applied to investigate the appropriate probability distribution for each time-scale, and the normality of the daily SPI time series derived from each probability distribution were compared. The Weibull distribution was suitable for calculating the daily SPI for short time-scales of 30 days or less, while the GEV distribution was suitable for longer time-scales of 270 days or more. However, overall, Gamma was found to be the best probability distribution. While there were some regional variations, the minimum time-scales that could be applied per season were as follows: 15 days for spring and summer, 21 days for fall, and 30 days for winter. It is shown that the minimum time-scale depends on how many zero values are included in the moving cumulative-precipitation time series, and it is shown that it is appropriate to have less than about 2.5%. Finally, the applicability of the GEV distribution is investigated.

MacroSheds: A synthesis of long‐term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies

AbstractThe US Federal Government supports hundreds of watershed monitoring efforts from which solute fluxes can be calculated. Although instrumentation and methods vary between studies, the data collected and their motivating questions are remarkably similar. Nevertheless, little effort toward their compilation has previously been made. The MacroSheds project has developed a future‐friendly system for harmonizing daily time series of streamflow, precipitation, and solute chemistry from 169+ watersheds, and supplementing each with watershed attributes. Here, we describe the breadth of MacroSheds data, and detail the steps involved in rendering each data product. We provide recommendations for usage and discuss when other datasets might be more suitable. The MacroSheds dataset is an unprecedented resource for watershed science, and for hydrology, as a small‐watershed supplement to existing collections of streamflow predictors, like CAMELS and GAGES‐II. The MacroSheds platform includes a web dashboard for visualization and an R package for data access and analysis.

Data quality control and homogenization of daily precipitation and air temperature (mean, max and min) time series of Ukraine

AbstractIn this paper we present the results of quality control and homogenization procedures applied to long time series of daily atmospheric precipitation sums (Rr) and daily mean (Tm), maximum (Tx) and minimum (Tn) air temperature collected in Ukraine. The daily data from 178 meteorological stations covering the period of 1946–2020 were analysed. In order to perform a thorough quality assurance check, we used the R package INQC, while the Climatol homogenization software was used to detect and remove breaks from the time series. The INQC quality assurance tests revealed a relatively small number of erroneous records (around 0.01% for each variable) and suspicious values (up to 0.09%). The application of Climatol resulted in 195, 296, 355 and 359 break points, detected for Rr, Tm, Tx and Tn, respectively. These quantities coincide roughly with the results of the HOMER homogenization procedure applied to monthly time series for the same stations and almost the same period (performed in the previous works of the authors). To verify the homogenization results, statistical comparison of the raw and homogenized time series was performed. The verification demonstrated that the quality control and homogenization procedures detected and removed errors and breaks very well, and air temperature and precipitation fields after the homogenization are more self‐consistent compared to the original raw data.

Impacts of Climate Change on Hydrological Regimes in the Congo River Basin

Surface water resources are essential for a wide range of human activities, such as municipal water supply, fishing, navigation, irrigation, and hydropower. Their regime is also linked to environmental sustainability, water-related risks, human health, and various ecosystem services. Global warming is expected to modify surface water availability, quality, and distribution and therefore affect water use productivity as well as the incidence of water-related risks. Thus, it is important for communities to plan and adapt to the potential impacts of climate change. The Congo River Basin, home to 75 million people, is subject to recurrent flood and drought events, which are expected to worsen as a result of climate change. This study aims to assess future modifications of the hydrological regime of the Congo River and the socio–economic impacts of these projected changes for three future periods: 2011–2041, 2041–2070, and 2071–2100. A Soil and Water Assessment Tool (SWAT) model of the Congo River Basin was developed, calibrated, and validated using daily rainfall observations combined with daily time series of precipitation, temperatures, relative humidity, solar radiation, and wind speed derived from the WFDEI (Watch Forced Era Interim) reanalysis data set. The outputs of ten Regional Climate Models (RCMs) from the Coordinated Downscaling Experiment (CORDEX-AFRICA) were statistically downscaled to obtain future climate time series, considering two Representative Concentration Pathways: RCP8.5 and RCP4.5. The calibrated model was used to assess changes in streamflow in all reaches of the Congo River. Results suggest relative changes ranging from −31.8% to +9.2% under RCP4.5 and from −42.5% to +55.5% under RCP 8.5. Larger relative changes occur in the most upstream reaches of the network. Results also point to an overall decrease in discharge in the center and southern parts of the basin and increases in the northwestern and southeastern parts of the basin under both emission scenarios, with RCP8.5 leading to the most severe changes. River discharge is likely to decrease significantly, with potential consequences for agriculture, hydropower production, and water availability for human and ecological systems.

Performance Evaluation of an ANN based Bias Correction algorithm in Monthly and Daily Precipitation Time Series of La Farge Station, USA

Understanding the change of future precipitation over long run is highly necessary in climate change impact studies. Mostly, simulated future precipitation series are found to be biased more with the historically observed precipitation series which need to be corrected before use for any impact studies. Many conventional and data-driven methods are available to correct this bias. In this study, to bias correct the monthly and daily precipitation series, Artificial Neural Network based method is applied and compared with the conventional methods. The normalized root mean squared errors obtained for monthly and daily series are 0.786 and 2.55 respectively. It is found that the performance of ANN-based method is poor in daily series and good only in monthly series. The reason for poor performance in daily series is analysed. In addition, the superiority of ANN based method over conventional method is established in monthly precipitation time series.

Homogenised Monthly and Daily Temperature and Precipitation Time Series in China and Greece since 1960

In this paper, we describe and analyze two datasets entitled “Homogenised monthly and daily temperature and precipitation time series in China during 1960–2021” and “Homogenised monthly and daily temperature and precipitation time series in Greece during 1960–2010”. These datasets provide the homogenised monthly and daily mean (TG), minimum (TN), and maximum (TX) temperature and precipitation (RR) records since 1960 at 366 stations in China and 56 stations in Greece. The datasets are available at the Science Data Bank repository and can be downloaded from https://doi.org/10.57760/sciencedb.01731 and https://doi.org/10.57760/sciencedb.01720. For China, the regional mean annual TG, TX, TN, and RR series during 1960–2021 showed significant warming or increasing trends of 0.27°C (10 yr)−1, 0.22°C (10 yr)−1, 0.35°C (10 yr)−1, and 6.81 mm (10 yr)−1, respectively. Most of the seasonal series revealed trends significant at the 0.05 level, except for the spring, summer, and autumn RR series. For Greece, there were increasing trends of 0.09°C (10 yr)−1, 0.08°C (10 yr)−1, and 0.11°C (10 yr)−1 for the annual TG, TX, and TN series, respectively, while a decreasing trend of −23.35 mm (10 yr)−1 was present for RR. The seasonal trends showed a significant warming rate for summer, but no significant changes were noted for spring (except for TN), autumn, and winter. For RR, only the winter time series displayed a statistically significant and robust trend [−15.82 mm (10 yr)−1]. The final homogenised temperature and precipitation time series for both China and Greece provide a better representation of the large-scale pattern of climate change over the past decades and provide a quality information source for climatological analyses.

Global patterns of water storage in the rooting zones of vegetation

The rooting-zone water-storage capacity—the amount of water accessible to plants—controls the sensitivity of land–atmosphere exchange of water and carbon during dry periods. How the rooting-zone water-storage capacity varies spatially is largely unknown and not directly observable. Here we estimate rooting-zone water-storage capacity globally from the relationship between remotely sensed vegetation activity, measured by combining evapotranspiration, sun-induced fluorescence and radiation estimates, and the cumulative water deficit calculated from daily time series of precipitation and evapotranspiration. Our findings indicate plant-available water stores that exceed the storage capacity of 2-m-deep soils across 37% of Earth’s vegetated surface. We find that biome-level variations of rooting-zone water-storage capacities correlate with observed rooting-zone depth distributions and reflect the influence of hydroclimate, as measured by the magnitude of annual cumulative water-deficit extremes. Smaller-scale variations are linked to topography and land use. Our findings document large spatial variations in the effective root-zone water-storage capacity and illustrate a tight link among the climatology of water deficits, rooting depth of vegetation and its sensitivity to water stress.

CLIMBra - Climate Change Dataset for Brazil

General Circulation and Earth System Models are the most advanced tools for investigating climate responses to future scenarios of greenhouse gas emissions, playing the role of projecting the climate throughout the century. Nevertheless, climate projections are model-dependent and may show systematic biases, requiring a bias correction for any further application. Here, we provide a dataset based on an ensemble of 19 bias-corrected CMIP6 climate models projections for the Brazilian territory based on the SSP2-4.5 and SSP5-8.5 scenarios. We used the Quantile Delta Mapping approach to bias-correct daily time-series of precipitation, maximum and minimum temperature, solar net radiation, near-surface wind speed, and relative humidity. The bias-corrected dataset is available for both historical (1980–2013) and future (2015–2100) simulations at a 0.25° × 0.25° spatial resolution. Besides the gridded product, we provide area-averaged projections for 735 catchments included in the Catchments Attributes for Brazil (CABra) dataset. The dataset provides important variables commonly used in environmental and hydroclimatological studies, paving the way for the development of high-quality research on climate change impacts in Brazil.

Gap-filling of daily precipitation and streamflow time series: a method comparison at random and sequential gaps

ABSTRACT This study compared the performance of seven gap-filling methods in daily streamflow and precipitation data and assessed the maximum number of gap days on which the methods perform well. Random (occurring randomly in the data series) and sequential (consecutive days with missing record) gaps were considered. Results show that the type of gaps affects the performance of the methods for gap-filling streamflow and precipitation data. Concerning random gaps, the best methods for streamflow were autoregressive integrate moving average and spline interpolation. For precipitation, the best methods were inverse distance weighting and linear regression (LR). Regarding sequential gaps, LR and multiple regression perform well in gap-filling up to 60 consecutive days in streamflow series. The other methods perform well up to 15 consecutive gap days. For precipitation series, the methods performed well up to seven consecutive missing days. For longer gaps (15, 30, 45 and 60 days), the methods performed poorly.

Using a Simple Methodology to Assess the Acceleration in Daily Precipitation Extreme Events in the São Paulo Metropolitan Region

This article analyses a near-centennial time series of daily precipitation in the Metropolitan Region of São Paulo, Brazil, in order to quantify the detectable increase in intensity and/or frequency of extreme events. This area is the most populated in the southern hemisphere, and heavy or extreme precipitation events, mainly those related with hydro-meteorological disasters, have important effects on its society. Indexes derived from daily precipitation data through a simple methodological approach are able to quantify changes at decadal and annual time scales. The analysis was carried out for five thresholds, i.e., daily precipitation higher than 50, 60, 70, 80, and 90 mm. The indexes exhibited statistically trends in both precipitation intensity and frequency for all thresholds, indicating significant changes in daily extreme events in the study period.

Evaluation of the spatial characteristics of climate scenarios based on statistical and dynamical downscaling for impact assessments in Japan

AbstractRepresentation of spatial dependence on temporal variations in climate scenarios is quite important when performing impact assessments in various sectors, including water resource management, agriculture, and energy. Although the complex terrain of Japan is closely related to the formation of regional climates, the spatial aspects of climate scenarios have not been investigated. To examine how well spatial dependency on temporal variations in precipitation is represented, we analysed the dependence of the correlation of daily precipitation time series for station pairs on the geographical distance between stations in several downscaled products for Japan. Although the bias correction (BC) method using quantile mapping was sufficient for removing the inherent biases at each station, we found minimal improvement in the spatial dependency. The performance was strongly dependent on the grid spacing of the parent model when the downscaling process was simple; it could not be improved by applying BC to the model output statistics. When analogue‐type statistical downscaling was applied, the observed spatial pattern of interstation correlations was much better reproduced; this was comparable to the results obtained using a dynamical downscaling technique with grid spacing of a few kilometres. Our results emphasize the importance of selecting appropriate data to represent the spatial variability of meteorological variables because mean bias‐based or accessibility‐based data selection may produce misleading results. The combination of an analogue method with BC of model output statistics has the potential to effectively represent the marginal and spatial aspects of climate scenarios.

Climate variability impacts on runoff projection under quantile mapping bias correction in the support CMIP6: An investigation in Lushi basin of China

In this study, we modelled the XAJ (Xin’anjiang) hydrologic model parameters for the Lushi basin of China, an important tributary of the Yellow River. Time series of daily precipitation, evaporation, and runoff data from 1976 to 1995 were used to calibrate the model, while the data from 1996 to 2000 were used for validation. Thereafter, the future climate variability was projected from 2021 to 2100 for 0.22° grids under CMIP6 CORDEX-East-Asia (Coordinated Regional Climate Downscaling Experiment-East-Asia) RCP2.6 and RCP8.5 climate scenarios. The non-parametric quantile mapping (QM) bias correction method was used to minimize the large differences between climate variability directly derived from the regional circulation model (RCM) and the historical observations, and the results were also compared with the baseline period (1976–2000). Overall, when compared to the baseline case, results showed a decline in the annual precipitation by 7.22 % and 5.01 %, while an increase was observed for the annual evaporation by 2.03 % and 3.58 % under RCP2.6 and RCP8.5 climate scenarios in the 21st century. Annual runoff projection by XAJ model indicate reduction, with 9.3 % and 31.2 % fall for short-term (2021–2040), 31.5 % and 41.2 % fall for mid-term (2051–2070) and 32.3 % and 77.4 % fall for long-term (2081–2100), respectively. The extreme streamflow projects increase during the dry period (November to June) with 76.5 m3/s and 84.6 m3/s under RCP2.6 and RCP8.5 compared to 45.6 m3/s from the baseline period. In contrast, there is a decrease during the wet period (July to October) by 390 m3/s and 237 m3/s compared to 405 m3/s in the baseline data of the same period. Such projections become more pronounced toward the end of the 21st century, and the reduction is likely to continue if future climate projections occur. The hydrological parameters can be applied to adjacent and similar landscape basins for disaster forecasting and early warning. The QM method is effective in bias correction. The findings can also be used for flood and drought mitigation planning and water resources management in the Yellow River Basin.

Influence of Precipitation Characteristics and Vegetation on Runoff and Sediment: A Case on the Basin in the Three Gorges Reservoir Region

Sediment is the main carrier of pollutants in river channels. This study analyzed the distribution characteristics of precipitation, runoff, and sediment and their response characteristics in the Daning River basin. Based on daily precipitation (1979–2017), runoff (1989–2017), and sediment (1997–2017) time series, the Gini concentration index, precipitation concentration index (PCI), precipitation concentration degree (PCD), and precipitation concentration period were applied to assess the concentration characteristics of precipitation, runoff, and sediment on the daily, monthly, and seasonal scales. At each intensity level, precipitation was negatively correlated to the PCI and PCD. The normalized difference vegetation index (NDVI) values had strong negative correlations with rainy days with light precipitation (0.1–9.9 mm). The degrees of concentration were in the same order for the multiscale analysis: runoff < precipitation < sediment. Although the amount of daily precipitation of more than 25 mm displayed a significant increasing trend, suggesting an increased risk of flood and soil erosion, the significantly improved vegetation cover reduced the sediment-carrying capacity of the surface runoff, with significant decreases in the total amount and multiscale concentration degrees of sediment being observed. The results of the study provide a reference for the improvement of the potable water safety and ecological environment in the Three Gorges Reservoir region.

Time Series Features for Supporting Hydrometeorological Explorations and Predictions in Ungauged Locations Using Large Datasets

Regression-based frameworks for streamflow regionalization are built around catchment attributes that traditionally originate from catchment hydrology, flood frequency analysis and their interplay. In this work, we deviated from this traditional path by formulating and extensively investigating the first regression-based streamflow regionalization frameworks that largely emerge from general-purpose time series features for data science and, more precisely, from a large variety of such features. We focused on 28 features that included (partial) autocorrelation, entropy, temporal variation, seasonality, trend, lumpiness, stability, nonlinearity, linearity, spikiness, curvature and others. We estimated these features for daily temperature, precipitation and streamflow time series from 511 catchments and then merged them within regionalization contexts with traditional topographic, land cover, soil and geologic attributes. Precipitation and temperature features (e.g., the spectral entropy, seasonality strength and lag-1 autocorrelation of the precipitation time series, and the stability and trend strength of the temperature time series) were found to be useful predictors of many streamflow features. The same applies to traditional attributes such as the catchment mean elevation. Relationships between predictor and dependent variables were also revealed, while the spectral entropy, the seasonality strength and several autocorrelation features of the streamflow time series were found to be more regionalizable than others.

Rainfall Patterns From Multiscale Sample Entropy Analysis

Precipitation is a manifestation of many interacting complex processes. How to grasp its temporal pattern that would reveal underlain dominant contributing factors is the key objective of the study. For this, we explored the application of multiscale sample entropy (MSE) in describing the long-term daily precipitation. Sample entropy (SE) adds similarity measure over the conventional information entropy, and it has been used in quantifying changing complexity in chaotic dynamic systems. With the further incorporation of multiscale consideration, the MSE analysis gives the trend of SE changes with scale, and provides a rich description of participating factors. The daily precipitation time series studied were taken from 665 weather stations across China that have been recorded for about 50–61 years. The SE estimates are a function of the length of time series (n), the dimension of similarity (m), and the match threshold (r). These parameters are problem-dependent, and through simulation, this study has determined that m = 2, r = 0.15, and n ≈ 20,000 would be appropriate for estimating SE up to the 30-day scale. Three general patterns of MSE for precipitation time series are identified: (1) Pattern A, SE increases with scale; (2) Pattern B, SE increases then decreases and followed by increase; and (3) Pattern C, SE increases then decreases. The MSE is found capable of detecting differences in characteristics among precipitation time series. Matching MSE thus could serve as a metric to evaluate the adequacy of simulated precipitation time series. Using this metric, we have shown that to embody seasonal changes one needs to use different monthly two-parameter gamma distribution functions in generating simulated precipitation time series. Moreover, for dry seasons, one also needs to consider interannual fluctuations: it is inadequate to use just one single function for simulating multi-year precipitation data. Finally, for the study region, MSE patterns show coherence over the distance in that stations that are close, which range from 40 to 80 km, exhibit similar MSE trends. The MSE patterns obtained are also found to be reflective of the regional precipitation patterns—this has important implications on water resources management.

Analysis of precipitation characteristics in Shanghai based on the visibility graph algorithm

Most of the existing studies on the characteristics of precipitation in Shanghai only focus on the statistical characteristics of the precipitation series rather than the internal dynamic characteristic. The visibility graph algorithm has been proven to be an effective algorithm for grasping the internal dynamics characteristics of time series. Therefore, this paper exploits the visibility graph algorithm to analyze the daily precipitation time series in Shanghai from 2000 to 2020. Some characteristics of precipitation during this period are obtained: Typhoon Mesa and Typhoon Fitow had a profound impact on Shanghai’s precipitation; There is a certain similarity of the precipitation structure in different periods; Only a few nodes in the precipitation network have a profound impact on the precipitation in Shanghai.

Stochastic downscaling of gridded precipitation to spatially coherent subgrid precipitation fields using a transformed Gaussian model

AbstractClimate impact models often require unbiased point‐scale observations, but climate models typically provide biased simulations at the grid scale. While standard bias adjustment methods have shown to generally perform well at adjusting climate model biases, they cannot overcome the gap between grid‐box and point scale. To overcome this limitation, combined bias adjustment and stochastic downscaling methods have been developed. These methods, however, are single‐site methods and cannot represent spatial dependence. Here we propose a multisite stochastic downscaling method that can be applied to bias‐adjusted climate model output for generating spatially coherent time series of daily precipitation at multiple stations, conditional on the driving climate model. The method is based on a transformed truncated multivariate Gaussian model and can also be used to downscale to a full field at finer‐grid resolution. An evaluation for stations across selected catchments in Austria demonstrates the good performance of the stochastic model at representing marginal, temporal and spatial aspects of daily precipitation, including extreme events.