High-resolution measurements and analyses of precipitation are crucial, especially in urban areas with high vulnerabilities, in order to describe the hydrological response and improve water risk management. Thus, this session also addresses contributions on the application of high-resolution precipitation data in hydrological impact and design studies.
Acting on this year's focus topic we emphasize the call for contributions on advancing atmospheric science, water management, and societal preparedness in a changing European climate with relation to hydro(meteoro)logical research and applications.
Summarizing, one or more of the following topics shall be addressed:
• Precipitation measurement techniques
• High-resolution precipitation observations from different platforms (e.g., gauges, disdrometers, radars, satellites, microwave links) and their combination
• Precipitation reference data sets (e.g., GPCC, OPERA)
• Drought monitoring and impact
• Statistical analysis of extreme precipitation (events)
• Statistical analysis of changes/trends in precipitation totals (monthly, seasonal, annual)
• Multi-scale analysis, including sub-kilometer scale statistical precipitation description and downscaling methods
• Definition and application of indices to characterize extreme precipitation events
• Climate change studies on extreme precipitation (events)
• Urban hydrology and hydrological impact as well as design studies
• New concepts of adaptation to climate change with respect to extreme precipitation in urban areas
• AI and ML techniques in hydrometeorological and hydrological research and applications
Estimates, based on a simple formula that approximates the probability of heavy 24-hr rainfall, give surprisingly high correlations with the observed number of days with more than 20 mm precipitation from rain gauge data around the world. A background for this formula is presented, which includes a series of past projects EU-SPECS, KlimaDigital, CORDEX FPS southeast Africa, and EU-SPRINGS. Although the initial analysis focussed on Norway, subsequent work has shown that this simple formula provides a reasonable description of the frequencies of heavy rainfall recorded by rain gauge data from all around the world where data has been available. Furthermore, a comparison with intensity-duration-frequency (IDF) analysis in Norway suggests that there is a fractal dependency between temporal scales that may be utilised in a simple IDF formula, and one question is whether this framework works elsewhere as it is related to the said formula for 24-hr precipitation. Even if such simple models are not sufficiently accurate for estimating design values, they may nevertheless be useful for benchmarking because they are easy to apply and require very little computational resources. The IDF framework has been tested on results from convection permitting regional climate model simulations to assess whether the fractal scaling varies in space. It may also provide a framework for downscaling future heavy precipitation statistics, both in terms of dynamical as well as empirical-statistical downscaling. A demonstration of both the formula for heavy 24-hr precipitation as well as for IDFs are incorporated in a test app for monitoring precipitation https://ocdp.met.no.
How to cite: Benestad, R., Dobler, A., Parding, K. M., and Lutz, J.: Testing some simple formulas for estimating the probability of heavy 24-hr precipitation and sub-daily return values, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-446, https://doi.org/10.5194/ems2026-446, 2026.
Improving our estimates of extreme precipitation is crucial for disaster preparedness, especially in a changing climate and at sub-daily or sub-hourly scales, as they are hardly resolved by current climate models and they are expected to change at faster rates. A recently proposed statistical approach (TEmperature-dependent Non-Asymptotic statistical model for eXtreme return levels, TENAX) has been designed to predict future sub-daily extremes using a physically-based dependence on near-surface temperature. Within this framework, a temperature model is also implemented to represent the probability of having a precipitation event at a given temperature. Such a physical dependence, partially inherited from the Clausius–Clapeyron relation, is well suited to a Bayesian approach.
Here, we present a Bayesian implementation of the TENAX model in which we investigate the added value of new physical covariates, we examine the possible priors based on physics knowledge, and we test both linear and exponential dependencies of the shape parameter on temperature and new formulations for the temperature model (e.g., Gaussian mixture, cyclostationary Gaussian). Results on several stations in Switzerland, Italy, Germany, Japan, the UK, and the USA are provided with quantitative uncertainty from the posterior samples, and show consistency of the past return levels with the previous TENAX model (which is based on maximum likelihood estimation with only the scale parameter dependent on temperature), and with other benchmark estimates. The dependence of the shape parameter (which is related to tail heaviness) on temperature is less trivial and may significantly affect the model’s accuracy. Future climate scenarios are also investigated within this framework.
How to cite: Darienzo, M., Canale, A., and Marra, F.: Including relations between extreme precipitation statistics and atmospheric variables within a Bayesian framework, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-268, https://doi.org/10.5194/ems2026-268, 2026.
Mesoscale convective systems (MCSs) are organized thunderstorm clusters in mid-latitude and tropical regions that can produce extreme precipitation threatening human safety and property. Under global warming, extreme precipitation is expected to increase in intensity and frequency over most regions, primarily due to increased moisture availability and convective available potential energy (CAPE). However, using a recently developed 4 km, hourly MCS-tracking dataset (Feng, 2024), we find that extreme precipitation produced by MCS over the central US shows decreasing trends during the warm seasons (April–September) of 2004–2021.
Extreme hourly MCS precipitation events are defined using the 95th percentile threshold (63 mm h-1). Over the study period, the number, mean rain rate, and total rainfall of extreme precipitation events all exhibit decreasing trends, with the decline in mean rain rate being statistically significant. Similar patterns are observed using the 90th (54.38 mm h-1) and 98th (73.63 mm h-1) percentiles, indicating robustness across thresholds.
To examine concurrent environmental changes, we extract sounding data from ERA5 and calculate thermodynamic and kinematic parameters. The results show that, despite increasing trends in instability (e.g., CAPE and lifting index) and downdraft CAPE (DCAPE), the decreasing MCS precipitation is associated with low-level drying and weakening of low-level vertical wind shear. Low-level relative humidity is found to decrease significantly, accompanied by rising lifted condensation level (LCL), indicating that the low-level atmosphere has become drier despite a slight (but not significant) increase in precipitable water. This suggests that warming has outpaced moistening. Meanwhile, both 0-1 km and 0-3 km vertical wind shear show consistent weakening trends.
Together, low-level drying and reduced vertical wind shear are likely to suppress organized MCSs capable of producing extreme precipitation under global warming. In addition, whether the overall extreme precipitation budget is compensated by isolated deep convection remains an open question for future research.
How to cite: Cao, D. and Xu, X.: Decreasing Warm-Season Extreme MCS Precipitation Over the Central US Under Global Warming: Associations with Low-Level Drying and Reduced Wind Shear, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-408, https://doi.org/10.5194/ems2026-408, 2026.
Accurate quantitative precipitation estimation (QPE) from X-band Polarimetric weather radar depends strongly on the quality of the input radar measurements. In Cyprus, observations produced by the Larnaca and Paphos X-band radars which are located close to Larnaca and Paphos International airports respectively and are retrieved from the Cyprus Department of Meteorology) provide valuable coverage for precipitation monitoring, however, low-signal conditions and noisy echoes can reduce the reliability of radar-derived products. In the present study, a quality-control framework based on horizontal signal-to-noise ratio (SNRh) is applied to improve the usability of reflectivity and selected Polarimetric fields for precipitation-related applications. SNRh is used as a primary indicator to identify and suppress low-confidence radar echoes prior to gridding analysis and further assimilation to numerical weather prediction model. The quality-controlled radar fields are then combined to generate a Cyprus-wide radar mosaic, providing an island-scale visualization of precipitation structures from both radars. This mosaic product represents better the spatial distribution of weather systems over Cyprus than the single-radar views alone and highlights the importance of consistent preprocessing prior to multi-radar integration. The results indicate that SNRh-based quality control is an effective and practical preprocessing step for reducing noise, improving the spatial consistency of radar fields, and supporting the generation of informative radar products. This investigation is conducted in the frames of the strategic infrastructure project CYGMEN (Cyprus GNSS Meteorology Enhancement) and provides a foundation for future radar-based rainfall retrieval, hybrid QPE algorithms, and operational multi-radar applications over Cyprus, which is characterized as a hot spot region in terms of climate change and extreme weather events.
How to cite: Hanmante, H., Oikonomou, C., and Haralambous, H.: SNRh-Based Quality Control and Cyprus-Wide Mosaic Generation from X-Band Polarimetric Radar Observations for QPE Applications, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-783, https://doi.org/10.5194/ems2026-783, 2026.
Accurate high-resolution quantitative precipitation estimation (QPE) using weather radar is essential for hydrological simulation and forecasting under increasing climate crisis. Although empirical Z-R relationships are widely used, their rainfall type-specific and regionally constrained nature limits generalization across diverse precipitation regimes. Data-driven machine learning offers a promising alternative, yet conventional regression-based approaches frequently struggle to preserve the spatial structure of radar imagery due to residual estimation artifacts.
To address these limitations, this study aims to develop a spatially coherent hybrid machine learning framework for radar QPE over South Korea, integrating three core methodological components. First, Gaussian Mixture Model (GMM) soft clustering is applied using radar reflectivity, terrain attributes, and Z-R derived variables, mitigating the boundary discontinuities inherent to hard-clustering approaches through probabilistic regime assignment. Second, cluster-specific Extreme Gradient Boosting (XGBoost) regressors trained with posterior probabilities as sample weights are merged through probability-weighted blending, enabling nonlinear Z-R mapping across heterogeneous precipitation regimes. Third, a binary mask derived from raw radar reflectivity thresholding is multiplied element-wise against the blended output, eliminating residual estimates over non-precipitating regions and restoring the spatial footprint of the original radar observation. For model training, we utilize Hybrid Surface Rainfall (HSR) composite reflectivity, Automated Synoptic Observing System (ASOS) rain gauges, and terrain data. The dense Automatic Weather Station (AWS) network serves as a robust independent validation set, ensuring spatial reliability across varied topography and rainfall intensities.
The framework is designed to capture nonlinear precipitation patterns across convective, orographic, and frontal regimes. Also, this approach minimizes the spatial uncertainty common in ML-based QPE outputs. The resulting framework maintains numerical continuity, contributing to the enhanced reliability of real-time hydrometeorological modeling and early warning operations.
This work was supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government [26ZR1300, Development of Technology for the Urban Extreme Rainfall Response Platform].
How to cite: Jung, S. and Chang, Y.-S.: A Spatially Coherent Hybrid Machine Learning Approach for Improving Radar Quantitative Precipitation Estimation, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-254, https://doi.org/10.5194/ems2026-254, 2026.
Floods occur frequently around the world and belong to the most dangerous meteorological hazards, causing destruction of infrastructure and loss of human life. Therefore, the estimation and nearcast of precipitation is of uttermost importance to launch warnings early enough.
Although radars are a reliable source for precipitation estimation, their coverage is not sufficient for comprehensive monitoring and near real-time observation of precipitation in many regions, e.g. radar data are largely unavailable over the ocean, hence largely blind regarding thunderstorms coming from the sea. Also over complex terrain and in remote areas radar information is only sparely available. Further, saturation effects hamper the accurate prediction of rain rates of large thunderstorms and finally rivers or catchment areas do not end close to national borders, but national radar networks do in contrast to satellite data.
Could satellite based precipitation data fill the gaps ? Up to now satellite based precipitation was not accurate enough to supplement radar data and to improve the spatial coverage and accuracy of precipitation rates around the world. Therefore, a novel method based on artificial intelligence has been developed to achieve an accuracy close to that of radar based rain rates.
In this method a precipitation rate is derived every ten minutes from the satellite-based effective cloud albedo by regression with rain gauge data (ombrometer). This approach provides a first near real time Quantitative Precipitation Estimation (QPE) to calculate satellite-based precipitation rates with a large geographical coverage. However, this approach shows shortcomings in regional differentiation as the regression is applied over all existing cloud types. In a second step, a method is therefore applied in order to learn from cloud structures (gradients, curvatures) and in this manner to take into account regional differences in the relationship between the retrieved cloud information and the precipitation rates. The method automatically transfers the learned relationship to regions without rain gauge data, a process that the authors refer to as spatial transfer learning. Thus, a meaningful QPE is also possible in areas without any rain gauge data (ombrometer), e.g. over the ocean and countries without radar data. Of course the method can also be applied to radar data and could improve the QPE from radar as well. Hence, the method will be also applied to radar.
The presentation will provide an overview about the developed method and the validation results with a focus on satellite based rain rates. However, the application to radar will be briefly discussed as well. The presentation will close with a brief outlook.
How to cite: Müller, R. and Hieronymus, M.: A novel approach for the estimation of precipitation from geostationary satellites and radar., EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-383, https://doi.org/10.5194/ems2026-383, 2026.
EURADCLIM is a publicly available climatological dataset of 1-h and 24-h precipitation accumulations covering 78% of geographical Europe at a 2-km grid. The newest version will contain data from the period 2013 – 2024. It is based on the surface rain rate composites from the EUMETNET programme OPERA. Non-meteorological echoes result in precipitation overestimation and are further removed by employing statistical methods but also using a satellite cloud type product. The 1-h accumulations are combined with rain gauge accumulations from the European Climate Assessment & Dataset (ECA&D). EURADCLIM is currently available at the KNMI Data Platform in ODIM HDF5 format but will become available at the Copernicus Climate Data Store in netCDF4 CF-1.8 format.
An overview of the employed input datasets and applied processing will be presented, as well as changes in processing for versions 3 and 4 with respect to previous versions. The performance of EURADCLIM precipitation accumulations is evaluated by comparisons to (independent) rain gauge data. This also entails a spatial evaluation, scatter density plots, and an evaluation of extremes. Specific attention will be given to outliers. The quality of EURADCLIM is clearly better than that of the original OPERA product. It is shown that EURADCLIM can be used to derive a pan-European precipitation climatology. Hence, EURADCLIM fills a gap by providing hourly precipitation accumulations, with a much higher spatial resolution and coverage than interpolated pan-European rain gauge datasets, which typically provide daily accumulations. This can be beneficial, for instance, for evaluating satellite precipitation products or weather model output, but also for better evaluation of extreme precipitation events and their impact (e.g., landslides, flooding).
How to cite: Overeem, A., van den Besselaar, E., van der Schrier, G., Meirink, J. F., van der Plas, E., and Leijnse, H.: The EURADCLIM gauge-adjusted radar precipitation dataset version 4, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-403, https://doi.org/10.5194/ems2026-403, 2026.
Founded in 1989, the Global Precipitation Climatology Centre (GPCC) provides globally gridded precipitation analyses based on in situ rain gauge measurements. During these years the precipitation database has been continuously expanded and includes a high station density and large temporal coverage. Today, the GPCC holds data from more than 129,000 stations, about three quarters of them having long time series.
The analyses are based mainly on data from the global meteorological and hydrological services, which provided their records to the GPCC, as well SYNOP and CLIMAT reports via the WMO-GTS. These form a supplement for the high-quality precipitation analyses and the basis for the near real-time evaluations. Due to the semi-automatic quality control routinely performed on the incoming station data, the GPCC database has a very high quality. Quality control activities include cross-referencing stations from different sources, flagging of data errors, and correcting temporally or spatially offset data. This data then serves as the basis for the subsequent interpolation and product generations.
Over the last months, new versions of three particular valuable data sets have been developed. The GPCC released a new version of its Climatology called „GPCC Precipitation Analysis Climatology Version 2025”, which includes 89’000 world wide stations (of which 3’000 have been added over the last 3 years). Further, the monthly dataset, starting in 1891, was reprocessed. This opportunity was used to harmonize the ‘Full Data Product’ with the ‘Monitoring Product’ and form the new „GPCC Precipitation Analysis Monthly Version 2025”. This ensures a combined product with regular monthly updates. In April 2026, GPCC will release also a new version of the former „Full Data Daily” Product, now „GPCC Precipitation Analysis Daily Version 2025”. It will cover 1982 – 2025 and will include many new stations in different regions, which improve the quality of the analysis, e.g., in Columbia and Italy.
These new products GPCC ‘Monthly’, “Daily” and ‘Climatology’ are freely available in netcdf format on the GPCC website https://gpcc.dwd.de and referenced by a digital object identifier (DOI). The web site also provides an overview of all datasets, as well as a detailed description and further references for each dataset.
This contribution focuses on introducing these new products and highlighting the changes and improvements compared to previous product versions.
How to cite: Rustemeier, E., Ziese, M., Schirmeister, Z., Finger, P., Heller, A., Schulze, R., Zepperitz, M., Fränkling, S., Jahn, M., and Breidenbach, J. N.: Global Precipitation Climatology Centre (GPCC) released new gridded precipitation analyses, that provide long-term means, monthly and daily totals, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-174, https://doi.org/10.5194/ems2026-174, 2026.
Sub-daily intensity duration frequency (IDF) curves underpin flood risk management and infrastructure design, yet they remain unavailable or highly uncertain in many Mediterranean and semi-arid regions due to sparse high-resolution gauge networks. Meanwhile, satellite precipitation products provide spatially continuous coverage but can exhibit systematic biases in magnitude, frequency, and extremes, particularly at short durations. This study examines whether machine-learning-adjusted, high-resolution satellite precipitation can support reliable sub-daily IDF estimation across an entire region, using Historical Palestine (Israel and the West Bank) as a climatically heterogeneous Mediterranean case study.
We propose a regional extreme-value framework that links bias-corrected satellite precipitation to sub-daily Intensity Duration Frequency (IDF) curves through a Peak-Over-Threshold approach with an Extended Generalized Pareto Distribution (POT-EGPD) parameterized using L-moments. Regionalization is performed using Gaussian Mixture Models (GMM), trained on calibration gauges only, with predictors combining extreme-shape information (L-moment ratios) and physiographic metadata (elevation, latitude, longitude, and climatic class). To evaluate generalizability, we implement station-based cross-validation, ensuring regional representativeness during splitting while preventing leakage.
We compare three methodological variants designed to isolate the effects of frequency and magnitude biases in satellite extremes: (i) a baseline regional POT model (U1) using calibration-gauge thresholds, tail parameters, and exceedance rates; (ii) a frequency-adjusted variant (U2) that corrects satellite exceedance rates using calibration-derived regional scaling; and (iii) an annual-maxima benchmark using regional GEV modelling (AM-GEV) with satellite index scaling. Satellite inputs include raw and machine-learning-adjusted products (e.g., IMERG raw versus IMERG adjusted via LightGBM).
Preliminary results indicate that machine-learning adjustment improves the consistency of satellite-based extreme DDF behaviour relative to gauges, and that explicitly correcting exceedance frequency further stabilizes tail behaviour across regions. The framework is intended for scalable regional IDF production in environments where fine-resolution gauges are scarce, supporting design-rainfall estimation and climate-resilient planning.
How to cite: Jayousi, F. and O'Loughlin, F.: A Cross-Validated Regional POT-EGPD Framework for Sub-Daily IDF Curves from Adjusted Satellite Precipitation in Mediterranean Climates, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-32, https://doi.org/10.5194/ems2026-32, 2026.
Weather radar with high spatiotemporal resolution is effective for heavy rainfall monitoring, as it enables real-time detection of precipitation system intensity, movement direction and speed, and provides coverage even in areas where rain gauge observations are unavailable. In particular, dual-polarization radar can distinguish hydrometeor types and their physical characteristics, allowing for more accurate precipitation estimation. With the increasing frequency and intensity of localized heavy rainfall events due to climate change, the Korea Meteorological Administration (KMA) sends emergency alert message (Cell Broadcasting Service, CBS) for extreme rainfall exceeding a certain threshold in affected areas. Accordingly, the demand for rapid and accurate radar-based precipitation information has become increasingly important.
In this study, a real-time radar based quantitative precipitation estimation (QPE) monitoring system was developed to assist forecasters in intuitively recognizing rainfall conditions and efficiently analyzing discrepancies between radar and rain gauge observations. The system provides real-time information on the accuracy and bias of radar-derived precipitation compared to rain gauge measurements, categorized by region, rainfall intensity, and accumulation duration.
In addition, a web-based interface enables visualization of relevant information for selected points or areas and provides time series comparisons between radar and gauge precipitation for individual stations. This system facilitates the analysis of precipitation trends and bias characteristics, supports decision-making for sending emergency alert messages, and contributes to the improvement of radar-based precipitation estimation techniques through validation using various severe weather cases.
Acknowledgements: This research was supported by the "Development of radar-based severe weather detection technology (KMA2026-00122)" of "Development of radar-based severe weather analysis technology project funded by the Weather Radar Center, Korea Meteorological Administration.
How to cite: Kim, H., Son, M., and Suk, M.-K.: User-Responsive Radar-Based Real-Time Precipitation Verification and Monitoring System, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-123, https://doi.org/10.5194/ems2026-123, 2026.
High-intensity rainfall often occurs on minute time scales and in rapidly changing precipitation regimes, which challenges both real-time monitoring and radar-based quantitative precipitation estimation (QPE). Radar retrieval relations implicitly rely on assumptions about drop size distributions (DSDs), yet DSDs can change substantially between convective rain, embedded convection within larger-scale precipitation, and stratiform rain affected by melting-layer processes. Here we use high-temporal-resolution disdrometer data as an independent microphysical reference to document these regime-dependent differences during intense events and to explore their potential for regime-adaptive QPE and event-based characterization.
We analyse minute-resolution observations from Prague, Czech Republic, measured by a 2D video disdrometer operated by the Institute of Atmospheric Physics of the Czech Academy of Sciences. The dataset covers 2011–2018 and comprises 710 minutes classified as strong convection, 162 minutes as embedded convection, and 418 minutes as stratiform rain with a bright band. Microphysical variability is summarized in the (Dm,log10Nw) phase space, where Dm is the mass-weighted mean diameter and Nw the normalized intercept parameter, and complemented by metrics sensitive to the large-drop tail.
The three regimes exhibit distinct and physically interpretable “fingerprints”. Strong convection is characterized by larger Dm, elevated Nw, and the highest contribution of large drops (typically above ~2–3 mm), consistent with intense coalescence and breakup dynamics during convective peaks. Stratiform rain with a bright band shows smaller characteristic sizes and markedly lower Nw, reflecting a different balance of microphysical processes linked to melting-layer precipitation. Embedded convection occupies an intermediate region and frequently alternates between convective-like and stratiform-like DSD states, making it the main source of ambiguity at minute scales.
To translate these differences into a practical regime indicator, we employ a linear Support Vector Machine trained on canonical end-members (strong convection vs bright-band stratiform) and interpret the signed distance to the boundary as a probabilistic “convective likelihood” with a transition band. This microphysical perspective provides added value for (i) regime-aware radar QPE assumptions during mixed events, and (ii) event-based indices that communicate not only how extreme an event is, but also what kind of extreme rainfall is occurring—an aspect relevant for urban hydrological impacts and preparedness in a changing European climate.
How to cite: Rulfova, Z. and Potuznikova, K.: Minute-scale disdrometer signatures of extreme rainfall in Prague (2011–2018): microphysical context for regime-adaptive radar QPE, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-225, https://doi.org/10.5194/ems2026-225, 2026.
The relationship theorized by Clausius-Clapeyron (CC) provides a thermodynamic baseline for how moisture content scales with temperature, and extreme precipitation could be expected to scale accordingly. Specifically, the CC equation predicts an increase in atmospheric water vapor holding capacity of approximately 7% per degree Celsius. Despite this theoretical basis, observed extreme precipitation often deviates from this scaling due to local atmospheric dynamics and specific event characteristics. In this study, we investigate precipitation-temperature (P-T) scaling across the Greater Alpine Region to better understand these deviations. We rely on a comprehensive observational dataset in which extreme precipitation is classified into distinct storm types, specifically separating convective-like from other types of events, such as stratiform rainfall. Using quantile regression, we explore the sensitivity of the P-T scaling rates to precipitation type and to the aggregation time interval used to define the temperature. Given the complex topography of the Alps, we analyze the spatial variability of the scaling relationship to assess how local climate and orography modulate the precipitation response. Preliminary results suggest that P-T scaling behaves differently depending on the dominant storm type, with convective-like rainfall exhibiting higher scaling rates (≈7%) compared to stratiform-like events (≈5%). Instead, when the two storm types are considered together, the scaling slope becomes steeper (≈10%). This occurs because convective and stratiform events are systematically distributed in different ways across the temperature range. By presenting this work, we hope to discuss how adopting an event-based typology can help refine our understanding of extreme precipitation drivers in mountain regions.
How to cite: Berton, M., Darienzo, M., and Marra, F.: Precipitation-Temperature scaling in the Alps: The role of storm typology and temperature aggregation, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-262, https://doi.org/10.5194/ems2026-262, 2026.
Recently, urban flooding has become more frequent due to short-duration, high-intensity rainfall. However, current design rainfall standards are based on limited observational records, which leads to large uncertainty when estimating extreme rainfall with long return periods.This study evaluates how design rainfall estimates change under increasingly variable future climate conditions. It focuses on differences in probability rainfall estimates depending on data sources and analytical methods, and examines how these differences affect the interpretation of recent flood-producing rainfall events. Busan was selected as the study area, and long-term hourly rainfall data from the ASOS station were used together with the large-ensemble climate dataset d4PDF (Database for Policy Decision-making for Future Climate Change). For the observational data, a parametric frequency analysis was applied, while for the d4PDF dataset, both nonparametric and parametric methods were used to estimate rainfall values for different return periods.In addition, major rainfall events that caused urban flooding in Busan between 2020 and 2025 were analyzed. The return periods of these events were estimated using the probability rainfall curves derived in this study, and the results from different data sources were compared.The results of this study suggest that the limitations of observation-based design rainfall can be improved by using large-ensemble climate data. This approach can provide useful information for urban flood risk assessment and for improving design standards under changing climate conditions.
Acknowledgments: The research for this paper was carried out under the KICT Research Program (Project no. 20260161–001, Development of Digital Urban Flood Control Technology for the Realization of Flood Safety City) funded by the Ministry of Science and ICT.
How to cite: Choi, G., Yoon, S.-S., Rhee, D. S., and Chung, I.-M.: Heavy Rainfall Frequency Analysis for Urban Flooding Using Rain Gauge Observations and d4PDF: A Case Study of Busan, South Korea, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-426, https://doi.org/10.5194/ems2026-426, 2026.
Short-duration extreme precipitation amounts (on both sub-daily and daily scales) are often required for the design and management of drainage systems at locations where no direct measurements or only limited records exist. Identifying regions with similar spatio-temporal characteristics of extremes can provide a more robust basis for estimating regional probabilities of rare events. Regional design values are expected to yield more reliable results than single-station analyses by reducing the influence of outliers, short records, and potential measurement errors. In this study, we apply a regional frequency analysis based on the L-moments approach to identify representative, homogeneous rainfall regions in Croatia. This task is particularly challenging due to the high spatial variability of precipitation maxima across the country, influenced by complex orography and diverse climatic conditions. Although regions with characteristic rainfall patterns have previously been identified, such classifications largely relied on the mean annual precipitation cycle and subjective criteria. The primary aim of this study is to establish a more objective and robust regionalization based on rainfall extremes. Due to the sparse network of rain gauges recording sub-daily precipitation, the analysis focuses on maximum daily precipitation amounts (Rx1d) derived from a dense network of 180 stations covering the period 1961–2022. A well-established four-step approach is applied, including data screening, identification of homogeneous regions based on geographical characteristics and distance from the sea, and the selection and estimation of the frequency distribution. By analysing different numbers of regions, ranging from three to nine, an optimal division into seven climatological regions was identified, providing the best representation of homogeneous characteristics of annual extreme daily precipitation. The generalized extreme value (GEV) distribution was identified as the most appropriate theoretical model for fitting Rx1d and was used to estimate return levels for individual stations as well as for regions. The characteristics of precipitation in the identified regions were further analysed in relation to prevailing weather types, providing additional insight into the physical drivers of extreme precipitation in these areas.
How to cite: Cindric Kalin, K., Lončar-Petrinjak, I., Pasarić, Z., Herceg Bulić, I., and Nimac, I.: Regional frequency analysis of daily extreme precipitation in Croatia, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-504, https://doi.org/10.5194/ems2026-504, 2026.
Reliable precipitation monitoring is crucial for hydrological and water resource management, particularly in semi-arid regions like Morocco where ground-based observation networks remain sparse. In this context, the present study evaluates and validates three distinct precipitation products, specifically the soil moisture-derived SM2RAIN-ASCAT, the reanalysis-based ERA5, and the satellite-based CHIRPS, against observed data from 36 synoptic stations distributed across Morocco's diverse climatic zones over the period 2007–2022. Different results for the different timescales have emerged, with performance differing markedly by both source and temporal scale. Despite strong detection capabilities, ERA5 showed the strongest overall performance, achieving the highest correlation and probability of detection (POD) throughout the study domain. In contrast, SM2RAIN-ASCAT exhibited a systematic overestimation, while CHIRPS showed a widespread tendency toward underestimation. Based on daily assessments, all products showed poor accuracy and elevated false alarm ratios, in particular during the dry summer months (JJA), where convective and sparse rainfall makes precise satellite retrieval especially challenging. A Quantile Mapping (QM) bias correction methodology was applied to address these disparities and improve the quantitative reliability of each dataset. The correction revealed that, particularly at the monthly and seasonal scales, the explained variance (R²) for ERA5 and CHIRPS increased significantly (R² > 0.6), indicating a tighter alignment with ground observations. While SM2RAIN-ASCAT showed improved consistency following correction, it remained less reliable than ERA5 and CHIRPS in representing temporal dynamics across the study area. Finally, these results highlight that bias-corrected ERA5 and CHIRPS are the most dependable sources for hydrological applications in Morocco, while bias-corrected SM2RAIN-ASCAT stands as a particularly valuable alternative for monthly assessments in data-scarce environments where soil moisture-based retrieval offers a distinct and independent estimation pathway.
How to cite: El Goumi, S., Koohi, S., Bouras, E. H., EL Alaouy, N., Guendour, R., Nait-Taleb, O., Essbiti, M. C., Chouidda, H., Krimissa, S., Elaloui, A., and Namous, M.: Toward Reliable Precipitation Estimation in Data-Scarce Semi-Arid Regions: A Multi-Source Evaluation and Bias Correction Study over Morocco, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-37, https://doi.org/10.5194/ems2026-37, 2026.
Statistically sound extreme‑value analysis is essential for modelling the return times of heavy rainfall events. To obtain statistically robust return times, long and homogeneous time series are required. Even with such series, the estimated return times are only valid for the data on which the statistics are derived. This makes it difficult to compare return times from different data sources and to classify the return times of forecasts, for which usually no sufficiently long time series exist. Nevertheless, reliable and comparable return times of heavy rainfall are crucial for design precipitation and for impact-oriented warnings.
Our aim is therefore to develop a method that enables a statistically consistent comparison of return levels from different datasets and, subsequently, to estimate return times of forecast precipitation values on a solid statistical basis.
We use three datasets: (1) spatially and temporally homogenized multiannual radar-based precipitation estimates (RADKLIM), (2) reanalysis data (COSMO‑REA6), and (3) the official rain gauge based design‑precipitation dataset for Germany (KOSTRA‑DWD2020). Because of its high quality and its status as the reference for hydraulic infrastructure in Germany, KOSTRA‑DWD2020 is regarded as the reference data. In a first step we compared the three datasets by analyzing their distributions, return times, and return levels. This comparison revealed a systematic, duration‑dependent underestimation of both RADKLIM and COSMO‑REA6 relative to KOSTRA‑DWD2020. Consequently, we are developing methods to correct this underestimation. Various approaches are being tested, such as the use of duration‑dependent correction factors or a duration‑spanning distribution‑mapping technique.
The resulting adjusted return times may be incorporated as additional information in effective warnings for heavy‑rainfall events. In the future, we plan to combine these return times with further impact‑oriented data to develop more comprehensive, impact‑oriented products.
How to cite: Linder, M., Walawender, E., Lengfeld, K., and Winterrath, T.: Towards a consistent extreme value statistic across heterogeneous precipitation data, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-644, https://doi.org/10.5194/ems2026-644, 2026.
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