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Temperature increases are associated with an intensification of heavy sub‐daily extreme rainfall by approximately 7% per °C, in accordance with the Clausius‐Clapeyron (CC) relation. As a result of this intensification, there are concerns regarding the increased frequency and magnitude of floods in small to medium‐sized catchments. The high‐resolution two‐dimensional weather generator (WG), AWE‐GEN‐2d, offers an ideal tool to simulate the climate variables required to assess the catchment‐scale hydrological response at high resolution. However, it lacked an explicit representation of the relationship between temperature and precipitation that can mimic the CC relationship. Therefore, we introduce a newly revised version of the model, named AWE‐GEN‐2d‐CC, designed to mirror the observed CC scaling by conditioning the simulation of precipitation properties (intensity and area) on temperature. We demonstrate the model's efficacy in representing future extreme rainfall by simulating their potential impact on the hydrological response of a mountainous catchment in the Swiss Alps. Based on observations and future climate model projections, AWE‐GEN‐2d‐CC was used to generate large ensembles of present and end‐of‐century climate data at hourly and 2‐km resolutions, used subsequently as input to Topkapi‐ETH, a physically‐based, distributed hydrological model. The new version of the WG successfully mimics the CC scaling of heavy rainfall, leading to an intensification of short‐duration heavy rainfall in future climates, in contrast with the results obtained using the original model. This allows for a more realistic assessment of future rainfall impact on hydrological response, which, in the demonstration application, shows a modulated effect even for short durations. Plain Language Summary: As global temperatures continue to rise, so does the intensity of heavy rainfall, which can lead to increased flooding in small to medium‐sized areas. The challenge is predicting not only how heavy rainfall will intensify at a specific point, but also how it is distributed over a catchment area. The AWE‐GEN‐2d model proved to be an effective tool in this respect. We introduce a new version of that model, called AWE‐GEN‐2d‐CC, which explicitly considers the relationship between temperature and rainfall. We tested our model on a mountainous area in the Swiss Alps, using past observations and future climate model projections, and found that the model can successfully predict heavy rainfall intensification in the future, especially for short, intense bursts of rain. This improves the plausibility of the results compared to those obtained with the older version of the model. Despite the predicted increase in heavy rainfall, we found the effect on river flows in the catchment was less drastic. Our study highlights the importance of accurately modeling the impact of temperature on rainfall. In areas prone to high‐intensity storms, our model can assist stakeholders in preparing for future weather extremes. Key Points: A new version of the AWE‐GEN‐2d weather generator model is presentedThe new model explicitly simulates the temperature dependency of extreme precipitation, enabling a realistic simulation of future extremesA climate change hydrological impact study over an alpine catchment is presented [ABSTRACT FROM AUTHOR]

Fast urban pluvial flood models are necessary for a range of applications, such as near real-time flood nowcasting or processing large rainfall ensembles for uncertainty analysis. Data-driven models can help overcome the long computational time of traditional flood simulation models, and the state-of-the-art models have shown promising accuracy. Yet the lack of generalizability of urban pluvial flood data-driven models to both terrain and rainfall events still limits their application. These models usually adopt a patch-based framework to overcome multiple bottlenecks, such as data availability and computational and memory constraints. However, this approach does not incorporate contextual information of the terrain surrounding the small image patch (typically 256 m x 256 m). We propose a new deep-learning model that maintains the high-resolution information of the local patch and incorporates a larger context to increase the visual field of the model with the aim of enhancing the generalizability of urban pluvial flood data-driven models. We trained and tested the model in the city of Zurich (Switzerland), at a spatial resolution of 1 m, for 1-hour rainfall events at 5 min temporal resolution. We demonstrate that our model can faithfully represent flood depths for a wide range of rainfall events, with peak rainfall intensities ranging from 42.5 mm h-1 to 161.4 mm h-1. Then, we assessed the model's terrain generalizability in distinct urban settings, namely Luzern (Switzerland) and Singapore. The model accurately identifies locations of water accumulation, which constitutes an improvement compared to other deep-learning models. Using transfer learning, the model was successfully retrained in the new cities, requiring only a single rainfall event to adapt the model to new terrains while preserving adaptability across diverse rainfall conditions. Our results indicate that by incorporating contextual terrain information into the local patches, our proposed model effectively generates high-resolution urban pluvial flood maps, demonstrating applicability across varied terrains and rainfall events. [ABSTRACT FROM AUTHOR]

Climate change is expected to influence future agricultural water availability, posing particular challenges in rainfed agricultural systems. This study aims to analyze the climatology of green water availability and water-limited attainable yields (AY) – the maximum crop yield achieved with available green water under optimal soil nutrient and crop management, considering four major cereal crops (teff, maize, sorghum, and wheat) produced in Ethiopia. An agrohydrological modelling framework was developed to simulate climatic-hydrological-crop interactions. The model was applied to a reference (1981–2010) and future periods (2020–2099) under low, intermediate, and high greenhouse gas emission scenarios in order to: (i) evaluate the current green water availability and AY potential; (ii) assess their climate-driven changes; and (iii) analyze the sensitivity of changes in AY to changes in rainfall and atmospheric evaporative demand. With regional variations based on climatic regimes, the main growing season (Meher, May to September) has an average AY of 79 % of a fully irrigated potential yield with an average soil moisture deficit of 29 % of moisture content at full water holding capacity. AY of the short growing season (Belg, February–May) is on average 37 % of the potential yield, with a soil moisture deficit of 56 %. Under the future climate, Meher is expected to experience small changes in the range of ±5 % with dominantly positive trends in the 2030s and decreases in the 2060s and 2080s, mainly driven by changes in the atmospheric evaporative demand due to rising temperatures. The Belg-producing regions are expected to experience increased AY that is dominantly controlled by increases in rainfall. On the other hand, a substantial yield gap is identified between actual and water-limited yields. This points to the need for combining green water management practices with nutrient and tillage management, plant protection, and cultivar improvement to close the yield gaps and to build up climate resilience of farmers. [ABSTRACT FROM AUTHOR]

Extreme sub-hourly precipitation, typically convective in nature, is capable of triggering natural disasters such as floods and debris flows. A key component of climate change adaptation and resilience is quantifying the likelihood that sub-hourly extreme precipitation will exceed historical levels in future climate scenarios. Despite this, current approaches to estimating future sub-hourly extreme precipitation return levels are deemed insufficient. The reason for this can be attributed to two factors: there is limited availability of data from convection-permitting climate models (capable of simulating sub-hourly precipitation adequately) and the statistical methods we use to extrapolate extreme precipitation return levels do not capture the physics governing global warming. We present a novel physical-based statistical method for estimating the extreme sub-hourly precipitation return levels. The proposed model, named TEmperature-dependent Non-Asymptotic statistical model for eXtreme return levels (TENAX), is based on a parsimonious non-stationary and non-asymptotic theoretical framework that incorporates temperature as a covariate in a physically consistent manner. We first explain the theory and present the TENAX model. Using data from several stations in Switzerland as a case study, we demonstrate the model's ability to reproduce sub-hourly precipitation return levels and some observed properties of extreme precipitation. We then illustrate how the model can be utilized to project changes in extreme sub-hourly precipitation in a future warmer climate only based on climate model projections of temperatures during wet days and on foreseen changes in precipitation frequency. We conclude by discussing the uncertainties associated with the model, its limitations, and its advantages. With the TENAX model, one can project sub-hourly precipitation extremes at different return levels based on daily scale projections from climate models in any location globally where observations of sub-hourly precipitation data and near-surface air temperature are available. [ABSTRACT FROM AUTHOR]

Extreme sub-hourly precipitation, typically convective in nature, is capable of triggering natural disasters such as floods and debris flows. A key component of climate change adaptation and resilience is quantifying the likelihood that sub-hourly extreme precipitation will exceed historical levels in future climate scenarios. Despite this, current approaches to estimating future sub-hourly extreme precipitation return levels are deemed insufficient. The reason for this can be attributed to two factors: there is limited availability of data from convective-permitting climate models (capable of simulating sub-hourly precipitation adequately), and the statistical methods we use to extrapolate extreme precipitation return levels do not capture the physics governing global warming. We present a novel physical-based statistical method for estimating the extreme sub-hourly precipitation return levels. The proposed model, named TEmperature-dependent Non-Asymptotic statistical model for eXtreme return levels (TENAX), is based on a parsimonious non-stationary and non-asymptotic theoretical framework that ncorporates temperature as a covariate in a physically consistent manner. We first explain the theory and present the TENAX model. Using data from several stations in Switzerland as a case study, we demonstrate the model's ability to reproduce sub-hourly precipitation return levels and some observed properties of extreme precipitation. We then illustrate how the model can be utilized to project changes in extreme sub-hourly precipitation in a future warmer climate only based on climate model projections of temperatures during wet days and on foreseen changes in precipitation frequency. We conclude by discussing the uncertainties associated with the model, its limitations, and its advantages. With the TENAX model, one can project sub-hourly precipitation extremes at different return levels based on daily-scale projections from climate models in any location globally where observations of sub-hourly precipitation data and near-surface air temperature are available. [ABSTRACT FROM AUTHOR]