This study presents a methodological framework to investigate the impacts of climate change on rainfall-triggered landslides at the subregional scale. Focusing on a ∼170 km² area in the Partenio Mountains in southern Italy, we employed regional rainfall projections (CORDEX) under moderate (RCP4.5) and high (RCP8.5) emission scenarios for 2006–2070. Rainfall data were bias corrected with observations from 2006–2023 and benchmarked against a synthetic dataset generated through stochastic reproduction of currently observed conditions. Physically based simulations of hydrological processes, coupled with slope stability analyses that account for unsaturated soil conditions, enabled event-by-event identification of landslides throughout the period. Statistical comparisons between scenarios were conducted across three rainfall homogeneous subregions. Results show a general tendency toward drier soil conditions, consistent with regional-scale climate studies, but with increasing rainfall variability across subregions. Despite this drying trend, projections indicate a significant rise in landslide occurrence, with a faster increase under RCP4.5 when compared to RCP8.5. This counterintuitive outcome reflects shifts in rainfall dynamics: under RCP8.5, landslides are mainly linked to more intense triggering rainfall, while under RCP4.5 they result from a combination of wetter antecedent conditions and more intense early-peak rainfall events. These findings emphasize the critical role of antecedent soil moisture in landslide initiation by showing its stable influence on landslide occurrence despite the rapid evolution of climate change. Overall, the methodology provides a transferable framework to assess local climate change impacts on geohazards by integrating bias-corrected climate projections with physically based hydrological–geomechanical modeling.

The complex composition of hydrological systems, climates and landscapes makes it challenging to explain and predict hydrological streamflow response. Many previous large-sample studies, mostly focused on the United States, identified climate as the primary control, with landscape exerting only a minor role in shaping hydrological behaviour. Yet, a few other studies report contradictory results with landscape being a more dominant driver. In this study, we use an unprecedented sample of more than 7000 catchments in Europe from the EStreams dataset to identify and map functionally similar catchments, together with their spatially variable climate and landscape controls. The wide spatial and temporal gradient of the study catchments was used to identify hydrological response types (HRTs) based on 40 hydrological streamflow signatures related to long-term averages and inter-annual variability of magnitude, timing, duration, frequency, and seasonality. Overall, 10 HRTs could be identified. Several HRTs are well defined and well distinguishable, largely due to catchments with strongly seasonal or more extreme behaviour. Other HRTs remain difficult to distinguish, as these catchments represent more transitional conditions with increasingly overlapping characteristics between HRTs. The underlying drivers of the HRTs were identified by using 84 climate- and landscape attributes to predict catchment membership to their respective HRT with a Random Forest classification model. Climate emerges as the dominant driver of hydrological behaviour at the continental scale. However, landscape was found, in 4 out of 10 HRTs, to be at least as strong or even stronger a control on the hydrological streamflow response. These results highlight that the complex, integrated nature of hydrological response remains challenging to disentangle, even with extensive datasets and advanced modelling approaches, and therefore, climate and landscape need to be understood as joint drivers in a co-evolutionary perspective.

Study Region: The study examines the Geul River basin in the southern Netherlands, a small (343 km²), hilly, rain-fed catchment characterised by a rapid rainfall-runoff response and exposure to both floods and droughts. Recent extremes, including the July 2021 flood and multiple dry summers, make the basin a relevant case study for understanding transitions between these opposite hydrological conditions. 

Study Focus: Transitions between drought-flood and flood-drought events were analysed using observations and hydrological simulations for 1982–2018. Floods and droughts were defined from meteorological thresholds and extended by an empirically derived catchment response time to capture hydrological effects. The role of antecedent precipitation, temperature, streamflow, soil moisture, and groundwater storage on subsequent event’s hydrological response was assessed using correlation and distribution (Kolmogorov–Smirnov) analyses. 

New Hydrological Insights for the Region: Antecedent droughts were associated with lower flood peaks, likely reflecting greater absorption of rainfall by dry soils prior to runoff generation. In contrast, antecedent floods were not associated with a consistent mitigation of streamflow response during subsequent droughts, consistent with continued streamflow recession from prior high flows. Groundwater showed a slower and more gradual adjustment than surface fluxes at the event scale, consistent with sub-seasonal storage buffering. Overall, opposite antecedent conditions in the Geul basin tend to moderate, rather than amplify, subsequent extremes. These findings emphasise the importance of considering antecedent catchment conditions and longer-term subsurface storage when evaluating flood and drought interactions in regional water management.

Large-sample hydrology datasets have become increasingly available, contributing to significant scientific advances. However, in Europe, only a few such datasets have been published, capturing only a fraction of the wealth of information from national data providers in terms of available spatial density and temporal extent. We present “EStreams”, an extensive dataset of hydro-climatic variables and landscape descriptors and a catalogue of openly available stream records for 17,130 European catchments. Spanning up to 120 years, the dataset includes streamflow indices, catchment-aggregated hydro-climatic signatures and landscape attributes (topography, soils, geology, vegetation and landcover). The catalogue provides detailed descriptions that allow users to directly access streamflow data sources, overcoming challenges related to data redistribution policies, language barriers and varied data portal structures. EStreams also provides Python scripts for data retrieval, aggregation and processing, making it dynamic in contrast to static datasets. This approach enables users to update their data as new records become available. Our goal is to extend current large-sample datasets and further integrate hydro-climatic and landscape data across Europe.

Quantifying the magnitude and frequency of extreme precipitation events is key in translating climate observations to planning and engineering design. Past efforts have mostly focused on the estimation of daily extremes using gauge observations. Recent development of high-resolution global precipitation products, now allow estimation of global extremes. This research aims to quantitatively characterize the spatiotemporal behavior of precipitation extremes, by calculating extreme precipitation return levels for multiple durations on the global domain using the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset. Both classical and novel extreme value distributions are used to provide insight into the spatial patterns of precipitation extremes. Our results show that the traditional Generalized Extreme Value (GEV) distribution and Peak-Over-Threshold (POT) methods, which only use the largest events to estimate precipitation extremes, are not spatially coherent. The recently developed Metastatistical Extreme Value (MEV) distribution, that includes all precipitation events, leads to smoother spatial patterns of local extremes. For durations of 5 and 10 days, however, there are less events per year to fit the distribution (37 and 22 on average, respectively), leading to larger inter-annual variability and possible overestimation of the extremes. While the GEV and POT methods predict a consistent shift from heavy to thin tails with increasing duration, the MEV method predicts a relatively constant heaviness of the tail for any precipitation duration, opening up an important research question on what is the ‘correct’ tail behavior of extreme precipitation for different durations. The generated extreme precipitation return levels and corresponding parameters are provided as the Global Precipitation EXtremes (GPEX) dataset. These data can be useful for studying the underlying physical processes causing the spatiotemporal variations of the heaviness of extreme precipitation distributions.

Global warming impacts the hydrological cycle, affecting the seasonality and timing of extreme precipitation. Understanding historical changes in extreme precipitation occurrence is crucial for assessing their impacts. This study uses relative entropy to analyze historical changes in seasonality and timing of extreme daily precipitation occurrences on the global domain for 63 years of fifth generation of the European Reanalysis reanalysis data. Our analysis reveals distinct regional patterns of change. During the second half of the 20th century, Africa and Asia experienced high clustering of precipitation extremes. Over the past 60 years, clustering increased in Africa while becoming more spread out in Asia. North America and Australia had initially lower clustering and showed slight increases over time. Extreme events in extra-tropical land regions mainly occurred in summer, with modest shifts in timing. These findings have implications for risk assessments of natural hazard like flash floods and landslides, emphasizing the necessity for region-specific adaptation strategies.