The Mediterranean region is strongly affected by Extreme Precipitation Events (EPEs), sometimes leading to severe negative impacts on society, economy, and the environment. Understanding such natural hazards and their drivers is essential to mitigate related risks. Here, EPEs over the Mediterranean between 1979 and 2019 are analyzed, using ERA5, the latest reanalysis dataset from ECMWF. EPEs are determined based on the 99^th percentile of their daily distribution (P99). The different EPE characteristics are assessed, based on seasonality and spatiotemporal dependencies. To better understand their connection to large‐scale atmospheric flow patterns, Empirical Orthogonal Function (EOF) analysis and subsequent non‐hierarchical K‐means clustering are used to quantify the importance of weather regimes to EPE frequency. The analysis is performed for different variables, depicting atmospheric variability in the lower and middle troposphere. Results show a clear spatial division in EPE occurrence, with winter and autumn being the seasons of highest EPE frequency for the eastern and western Mediterranean, respectively. There is a high degree of temporal dependencies with 20 % of the EPEs (median value based on all studied grid cells), occurring up to 1 week after a preceding P99 event at the same location. Local orography is a key modulator of the spatiotemporal connections and substantially enhances the probability of co‐occurrence of EPEs even for distant locations. The clustering clearly demonstrates the prevalence of distinct synoptic‐scale atmospheric conditions during the occurrence of EPEs for different locations within the region. Results indicate that clustering, based on a combination of sea level pressure (SLP) and geopotential height at 500 hPa (Z500), can increase the conditional probability of EPEs by more than three (3) times (median value for all grid cells) from the nominal probability of 1 % for the P99 EPEs. Such strong spatiotemporal dependencies and connections to large‐scale patterns can support extended‐range forecasts.

After the launch of the Global Precipitation Measurement (GPM) mission in 2014, many satellite precipitation products (SPPs) are available at finer spatiotemporal resolution and/or with reduced latency, potentially increasing the applicability of SPPs for near-real-time (NRT) applications. Therefore, there is a need to evaluate the NRT SPPs in the GPM era and investigate whether bias-correction techniques or merging of the individual products can increase the accuracy of these SPPs for NRT applications. This study utilizes five commonly used NRT SPPs, namely, CMOPRH RT, GSMaP NRT, IMERG EARLY, IMERG LATE, and PERSIANN-CCS. The evaluation is done for the Kinu basin region in Japan, an area that provides observed rainfall data with high accuracy in space and time. The selected bias correction techniques are the ratio bias correction and cumulative distribution function matching, while the merged products are derived with the error variance, inverse error variance weighting, and simple average merging techniques. Based on the results, all SPPs perform best for lower-intensity rainfall events and have challenges in providing accurate estimates for typhoon-induced rainfall (generally more than 50% underestimation) and at very fine temporal scales. Although the bias correction techniques successfully reduce the bias and improve the performance of the SPPs for coarse temporal scales, it is found that for shorter than 6-hourly temporal resolutions, both techniques are in general unable to bring improvements. Finally, the merging results in increased accuracy for all temporal scales, giving new perspectives in utilizing SPPs for NRT applications, such as flood and drought monitoring and early warning systems.

Extreme precipitation events (EPEs) can have devastating consequences, such as floods and landslides, posing a great threat to society and economy. Predicting such events long in advance can support the mitigation of negative impacts. Here, we focus on EPEs over the Mediterranean, a region that is frequently affected by such hazards. Previous work identified strong connections between localized EPEs and large‐scale atmospheric flow patterns, affecting weather over the entire Mediterranean. We analyse the predictive skill of these patterns in the European Centre for Medium‐Range Weather Forecasts (ECMWF) extended range forecasts and assess if and where these patterns can be used for indirect predictions of EPEs, using the Brier skill score. The results show that the ECMWF model provides skilful predictions of the Mediterranean patterns up to 2 weeks in advance. Moreover, using the forecasted patterns for indirect predictability of EPEs outperforms the reference score up to ∼10 days lead time for many locations. For high orography locations or coastal areas in particular, like parts of western Turkey, western Balkans, Iberian Peninsula, and Morocco, this limit extends to 11–14 days lead time. This study demonstrates that the connections between localized EPEs and large‐scale patterns over the Mediterranean extend the forecasting horizon of the model by over 3 days in many locations, in comparison with forecasting based on the predicted precipitation. Thus, it is beneficial to use the predicted patterns rather than the predicted precipitation at longer lead times for EPE forecasting. The model's performance is also assessed from a user perspective, showing that the EPE forecasting based on the patterns increases the economic benefits at medium/extended range lead times. Such information could support higher confidence in the decision‐making of various users; for example, the agricultural sector and (re)insurance companies.

This work compares forecasting strategies for improving the prediction of extreme precipitation events at different timescales. Our study focuses on forecasting extreme precipitation in Calabria, southern Italy. This region with complex and abruptly varying topography poses additional challenges in forecasting precipitation. Here, we use gridded observational precipitation data from the E‐OBS dataset, reanalysis data from ERA5, and forecasts from ECMWF reforecasts. We show that different forecasting horizons require different forecasting strategies. For short to medium‐range lead times, post‐processing the forecasted extreme precipitation probabilities provides the most informative outputs for end users. For extended‐range forecasts though, it is beneficial to use the large‐scale weather variability, as depicted based on nine Mediterranean patterns in combination with moisture‐related information, to infer reliable information about extreme precipitation. We present benefits and limitations of the methods based on long‐term statistical analysis using a range of indicators, such as the Brier skill score and its decomposition, and the cost–loss model.

While the evidence for anthropogenic climate change continues to strengthen, and concerns about severe weather events are increasing, global projections of regional climate change are still uncertain due to model-dependent changes in large-scale atmospheric circulation, including over North Atlantic and Europe. Here, the Jenkinson-Collison classification of daily circulation patterns is used to evaluate past and future changes in their seasonal frequencies over central Europe for the 1900–2100 period. Three reanalyses and eight global climate models from the coupled model intercomparison project, phase 6, were used based on daily mean sea-level pressure data. Best agreement in deriving relative frequencies of the synoptic types was found between the reanalyses. Global models can generally capture the interannual variability of circulation patterns and their climatological state, especially for the less frequent synoptic types. Based on historical data and the shared socioeconomic pathway 5 scenario, the evaluated trends show more robust signals during summer, given their lesser internal variability. Increasing frequencies were found for circulation types characterised by weak pressure gradients, mainly at the expense of decreasing frequencies of westerlies. Our findings indicate that, given a high-emission scenario, these signals will likely emerge from past climate variability towards the mid 21^st century for most altered circulation patterns.

The Meghna River basin is a transboundary basin that lies in Bangladesh (~40%) and India (~60%). Due to its terrain structure, the Bangladesh portion of the basin experiences frequent floods that cause severe human and economic losses. Bangladesh, as the downstream nation in the basin, faces challenges in receiving hydro-meteorological and water use data from India for effective water resource management. To address such issue, satellite rainfall products are recognized as an alternative. However, they are affected by biases and, thus, must be calibrated and verified using ground observations. This research compares the performance of four widely available gauge-adjusted satellite rainfall products (GSRPs) against ground rainfall observations in the Meghna basin within Bangladesh. Further biases in the GSRPs are then identified. The GSRPs have both similarities and differences in terms of producing biases. To maximize the usage of the GSRPs and to further improve their accuracy, several bias correction and merging techniques are applied to correct them. Correction factors and merging weights are calculated at the local gauge stations and are spatially distributed by adopting an interpolation method to improve the GSRPs, both inside and outside Bangladesh. Of the four bias correction methods, modified linear correction (MLC) has performed better, and partially removed the GSRPs’ systematic biases. In addition, of the three merging techniques, inverse error-variance weighting (IEVW) has provided better results than the individual GSRPs and removed significantly more biases than the MLC correction method for three of the five validation stations, whereas the two other stations that experienced heavy rainfall events, showed better results for the MLC method. Hence, the combined use of IEVW merging and MLC correction is explored. The combined method has provided the best results, thus creating an improved dataset. The applicability of this dataset is then investigated using a hydrological model to simulated streamflows at two critical locations. The results show that the dataset reproduces the hydrological responses of the basin well, as compared with the observed streamflows. Together, these results indicate that the improved dataset can overcome the limitations of poor data availability in the basin and can serve as a reference rainfall dataset for wide range of applications (e.g., flood modelling and forecasting, irrigation planning, damage and risk assessment, and climate change adaptation planning). In addition, the proposed methodology of creating a reference rainfall dataset based on the GSRPs could also be applicable to other poorly-gauged and inaccessible transboundary river basins, thus providing reliable rainfall information and effective water resource management for sustainable development.