Typhoon-induced Compound Flood (TCF), driven by the combined impact of extreme rainfall and increasing coastal water level (CWL), poses a substantial threat to urban safety. This study presents a framework for assessing the future compound flood hazard profiles in a coastal megacity in the Delta region of southern China. A coupled hydrology-hydrodynamic model is applied to simulate the flooding processes of 7 typhoon events. Scenarios are constructed using all possible pairwise combinations of three rainfall and three CWL conditions. These inputs are derived from statistical and dynamical downscaling of climate projections from the Coupled Model Intercomparison Project (CMIP6) ensemble under the SSP5-8.5 pathway. The results show that future CWL rise contributes more to future inundation than increasing rainfall, whereas rainfall contributions exhibit considerable uncertainties due to regional rainfall downscaling. Under extreme warming scenarios, future typhoons may produce increases of up to 230 mm in total rainfall and 28 mm per hour in rainfall intensity, which in turn increase the average urban inundation depth and area by 1.2 cm and 24.7 , respectively. Given an average CWL of 170 cm and a maximum CWL of 440 cm in the future, the inundation depth and area could increase by up to 8.4 cm and 29 , respectively. Within the 7 typhoons in this study, Hagupit (2014) exhibits the most notable compound effect, potentially expanding the medium-to-high risk area (inundation depth above 27 cm) by over 5%. This study demonstrates that climate change may intensify TCF, requiring flood-mitigating measures to consider rainfall-CWL interactions.

Climate change has the potential to significantly alter the characteristics of tropical cyclones (TCs). Understanding how representative extreme TCs with distinct meteorological structures and hydrological impacts respond to warming is critical for improving risk assessment. Super Typhoon Usagi (2013) represents a rare case, maintaining unusually high intensity at landfall while coinciding with an astronomical high tide-a combination infrequently observed in the climatological record-which led to severe compound flooding in coastal cities. However, how such exceptional TC characteristics respond to climate change remains unclear. In this study, we apply a high-resolution (5 km) Weather Research and Forecasting (WRF) model simulation within the PseudoGlobal Warming (PGW) framework, in which reanalysis-based initial and boundary conditions are perturbed by multi-variable warming signals from CMIP6 GCMs, to assess how climate change may alter the characteristics of Super Typhoon Usagi (2013). Our results indicate that the accumulated precipitation increases by up to 100 mm under future warming scenarios, along with an increase in peak intensity, range from 5 hPa (LESS_WARM) to 10 hPa (MORE_WARM)). Hourly precipitation is projected to rise by 6.5 %-26.4 %, exceed the temperatureinduced CC scaling (4.2 %-20.3 %). Increased latent heat flux (30-90 W m- 2) under warmer (0.6-2.9 K) and wetter (1.5-4.0 g kg- 1) climate conditions enhances TC intensification. Warming also affects the dynamic structure of TCs, enhancing vertical velocity (2-4 Pa s- 1) and tangential wind (5-10 m s- 1), expanding the inflow and outflow regions contributing to a stronger TC. The unexpected increase in precipitation is driven by both thermodynamic and dynamic factors. This case study provides insights into the potential responses of landfalling TCs-particularly those linked to compound flooding-in the Western Pacific Ocean (WNP) under future climate change.

Hydrodynamic surface runoff simulations are an effective method for assessing flash flood risks. In engineer ing, the lack of observations for model calibration poses a challenge. Therefore, understanding the sensitivity to specific model parameters is crucial for reliable flood protection planning. This study analyzes how surface dis cretization and roughness affect surface runoff generation and depression storage in a hydrodynamic 2D-model in a southern German alpine region. We compare the runoff generation across five discretization methodologies at 211 selected locations within the model domain. These locations are associated with subcatchments ranging in size from 0.2 to 4 km2. The discretization methodologies comprise a one-meter grid refined with survey data, a two-meter grid, a high-resolution and low-resolution irregular mesh and a four-meter grid. These are combined with seven different depth-dependent and constant roughness parameterizations. The sensitivity analysis shows that a higher depth-dependent roughness is needed to achieve comparable results to those of a coarse resolution model. Significant differences were observed with varying roughness pa rameterizations and meshing approaches. Modest alterations to surface resolution have the potential to yield deviations of up to 20% in maximum runoff. Coarser resolution models tend to create artificial depressions, leading to unrealistic water storage on hillsides.

Accurate representation of water vapour fluxes during extreme precipitation events by climate models is essential if we want to trust their future projections. Here, we examine the ability of the models from the Coupled Model Intercomparison Project of phase 6 (CMIP6) to represent the components of moisture fluxes, moisture static energy (MSE) budget, and atmospheric heating sources associated with extreme precipitation seasons in the Congo Basin. Our results reveal that the majority of CMIP6 models are in agreement with ERA5 in indicating that positive seasonal precipitation anomalies in the Congo Basin are controlled by vertical moisture advection induced by vertical velocity anomalies in both March-May (MAM) and September-November (SON) seasons. Analysis of the MSE balance shows that in MAM, vertical MSE advection induced by vertical motion is dominated in the southern part of the domain by horizontal MSE advection induced by MSE anomalies, while in the north, changes in the energy balance control vertical instability. However, it is important to emphasize that the positive anomalies of the horizontal MSE advection induced by the MSE anomalies are offset by the negative anomalies of the residual term, which weakens the atmospheric instability. In SON, there are significant contributions from the net energy balance and the residual term, mainly to the west of the Congo Basin and along the Gulf of Guinea. Conversely, the MSE budget is more noisy in both ERA5 and CMIP6 models and has an important residual term that suggests the linearization used here is insufficient to fully represent the budget. The investigation of the integrated atmospheric heating reveals that ERA5 simulates high values, especially in regions where heavy precipitation is recorded. Analysis of the CMIP6 models, including the ensemble mean model, similarly simulates high heating source values in extreme precipitation areas throughout the Congo Basin. The results of this study highlight the ability of the CMIP6 models to simulate the moisture and heating source balance during periods of extreme precipitation in the Congo Basin.

Water use efficiency (WUE) is a key indicator of ecosystem balance, reflecting how productivity responds to hydrological constraints under climate change. However, variability in WUE and its environmental drivers across West African agroecosystems remains poorly understood. Here, we integrate multi-year (2019–2024), half-hourly eddy-covariance observations of carbon and water-vapor fluxes from four contrasting land-use types in northern Ghana: a reserve savanna forest, rain-fed paddy rice, grassland, and rain-fed cropland. WUE exhibited pronounced diurnal and seasonal variability, shaped by hydrological, atmospheric, and land-management drivers. Diurnal patterns were bimodal, with morning and afternoon peaks shifting between wet and dry seasons. During the wet season, mean WUE was highest in the savanna forest (3.1 ± 0.26 g C kg⁻¹ H₂O), followed by paddy rice (2.08 ± 0.21), cropland (1.93 ± 0.20), and grassland (1.66 ± 0.18). Seasonal analyses highlighted ecosystem-specific controls, reflecting differences in radiation, soil moisture, and cultivation practices.

Freshwater flux over the Ca Mau Peninsula, Southern Vietnam, is crucial for sustaining regional food production, ecosystem functioning, and the livelihoods of communities that rely on agriculture and aquaculture. This study examines the sensitivity of two components of the freshwater flux, namely, simulated precipitation and potential evapotranspiration (PET), to planetary boundary layer (PBL) schemes and diurnal sea surface temperature (SST) variability using the Weather Research and Forecasting (WRF) model over the Ca Mau Peninsula. Convective gray-zone (5 km) simulations were conducted for three representative years corresponding to El Nino, ENSO (El Nino-Southern Oscillation) neutral, and La Nina conditions, using ERA5 boundary conditions. Three PBL schemes (Mellor-Yamada-Nakanishi-Niino Level 2.5: MYNN2.5, Mellor-Yamada-Janjic: MYJ and Asymmetric Convection Model 2: ACM2) were tested with or without the activation of the SST skin temperature option. Simulated precipitation was evaluated against rain gauge observations and satellite-based datasets, while PET was assessed using station-based estimates derived from the Penman-Monteith method and ERA5 reanalysis data. The results indicate that MYNN2.5 and MYJ reproduce observed rainfall patterns more accurately than ACM2, which exhibits a pronounced wet bias. Incorporating diurnal SST variability reduces precipitation biases by 10-40%. All schemes capture the general features of the diurnal rainfall cycle, although nighttime rainfall is systematically underestimated; among them, MYNN2.5 most accurately represents both inland and coastal rainfall peaks. PET simulations are also sensitive to the choice of PBL scheme, with MYNN2.5 showing the lowest overall bias. Overall, the MYNN2.5 scheme combined with the diurnal SST option provides the most reliable representation of the freshwater flux over the Ca Mau Peninsula. These findings offer important insights for improving freshwater flux simulations over the Ca Mau Peninsula, the most important aquaculture hub in Vietnam.

The Dragon Storm, which occurred between March 11 and 13, 2020, stands as the most intense cyclonic event in the past two decades, causing widespread torrential rainfall and flash flooding that resulted in over 20 fatalities across northeastern Egypt and the Sinai Peninsula (SiP). This study investigates the meteorological mechanisms behind the storm’s rapid intensification with a focus on a rare tropopause folding event, using a combination of satellite observations, reanalysis data, and numerical simulations. Our analysis reveals that, from a synoptic to regional dynamic perspective, the cyclone was notably intensified by a tropopause fold induced by an exceptional downward extension of stratospheric air with high potential vorticity into the lower troposphere, a phenomenon that coupled unexpectedly with the Earth’s surface across northeastern Egypt and the SiP region. Consequently, a typical mid-latitude Mediterranean cyclone evolved into a super-powerful dragon storm. Thermodynamic analysis showed that areas experiencing the highest precipitation (e.g., Cairo) coincided with strong atmospheric instability, characterized by significant water vapor mixing ratios, updrafts and low surface winds, accompanied by a moist, well-mixed troposphere with zero dewpoint depression in the lower atmosphere. These findings provide key insights into the processes driving the dragon storm’s intensification, and emphasize the increasing likelihood of similar events due to climate change.

Irrigation has a notable impact on the natural environment by changing the water and energy balance at the land surface and thereby altering atmospheric processes. Assessing these impacts and estimating irrigation water demand often involves using process-based models that incorporate the representation of irrigation practices. However, current irrigation schemes are primarily tailored to arid and semi-arid regions, and there is a research gap for humid multi-cropping rice regions. In response, this study introduces a Crop-specific Dynamic Irrigation (CDI) scheme, seamlessly integrated into the land surface-hydrologic model NOAH-HMS. This development enables the differentiation of irrigation practices for rice and non-rice crops, facilitating more accurate estimates of water demand for irrigation. The newly developed model is applied to an important cropping region in southern China, the Poyang Lake Basin (PLB), where the rice cultivation area accounts for over 60% of all crop cultivation. Compared to the widely used traditional Dynamic Irrigation (DI) scheme, integrating CDI into NOAH-HMS improves the model performance in simulating irrigation water amount over the PLB, with a mean relative error between 2007-2015 reduced by 39%, and a correlation coefficient increased by +0.26. The identified impacts on the surface water and energy balance are more pronounced at local scale, especially over the intensively irrigated areas. The performed interannual variability analysis demonstrates that our irrigation scheme CDI developed in this study allows to estimate irrigation water use under different drought conditions and has the applicability of mitigating risks of crop failures due to for example compound dry and hot. We conclude that our Crop-specific Dynamic Irrigation scheme is highly advantageous for multi-cropping rice regions and holds the potential for expansion into the fully coupled atmospheric-hydrologic systems with a more comprehensive representation of human activities.

Hydrological models are essential tools for water resource management and for mitigating extreme hydrological events risks. Although they are crucial for flood forecasting, these models often exhibit substantial uncertainties, including input data uncertainties (e.g., precipitation) and structural uncertainties of the models themselves. This study aims to explore the implications of different precipitation datasets and hydrological model structures on streamflow simulation, by evaluating the effects of multiple precipitation products and employing an enhanced model version to reduce structural uncertainty. This study evaluated the hydrological applicability of three representative precipitation products—reanalysis-based (the land component of the fifth-generation European Reanalysis, ERA5-Land), satellite-based (Integrated Multi-satellite Retrievals for GPM, IMERG), and machine learning-based (the first deep learning based spatio-temporal downscaling of precipitation data on a global scale, spateGAN-ERA5), using the offline version of WRF-Hydro, a distributed hydrological model. Additionally, this study evaluated the performance of an enhanced version of WRF-Hydro, incorporating an overbank flow module for reducing the model structural uncertainty in a large, flood-prone tropical river basin, Irrawaddy River Basin in Myanmar. The findings indicate that: (1) Simulations driven by IMERG precipitation outperformed those driven by ERA5-Land and spateGAN-ERA5 in terms of accuracy in streamflow, with average NSE values of 0.77, compared to 0.19 and 0.09, respectively; (2) The modified model with enabled overbank flow showed consistent improvements over the default model. The average NSE improved from 0.09–0.77 (default) to 0.31–0.78 (modified); (3) The water balance analysis reveals that incorporating the overbank flow module reduces surface runoff, accompanied by an increase in soil moisture storage, and slightly enhancing underground runoff and evapotranspiration (ET) during the rainy period. After the end of the rainy period, the increase soil moisture storage gradually contributes to an increase in surface runoff. These results highlight the significant impact of accurate precipitation data and the overbank flow module on hydrological processes, particularly in flood-prone areas, and suggest that the modified model and high quality precipitation data may enhance hydrological forecasting capabilities.

Global warming is accelerating the global water cycle. On a short temporal scale, such acceleration may modify weather regimes and, thus, potentially increase the number of compound weather and climate events. Among them, tropical cyclones can bring destructive high winds, torrential rain, storm surges and occasionally tornadoes in association with a variety of hazards, especially in coastal urban regions. In this study, we apply a newly developed WRF-age model, i.e., the Weather Research and Forecasting model enhanced with an age-weighted water tracking approach, to a coastal urban region in Southeast China. The source and transport of atmospheric water vapor in one Northwest Pacific Ocean cyclone, here, Hato in August 2017, are exemplarily examined by means of tracking oceanic evaporation. Two indices, i.e., the contribution ratio and the atmospheric water residence time, are used to better understand how much and how fast the oceanic evaporation contributes to the development of tropical cyclone Hato. Our simulation results show that, within 24 hours, the contribution ratio of the tagged oceanic evaporation to the total water vapor researches up to around 25%. In addition, the spatial pattern of the atmospheric water residence time shows that the oceanic evaporation below the rainbands of Hato (around 9 hours) fuels faster in its development than the oceanic evaporation from the surrounding region (15 hours). These findings emphasize the important role of oceanic evaporation to tropical cyclone development. Our study demonstrates that the WRF-age model can be applied to quantify the acceleration of tropical cyclone development under global warming.

Heatwaves are intensifying globally due to climate change. However, the contributions of large‐scale atmospheric processes and land‐atmosphere interactions to heatwave dynamics and their cascading impacts on water resources and human exposure are not fully understood. This study investigates heatwave frequency (HWF) across 50 global regions, spanning historical (1979–2014) and future periods (2025–2060 and 2065–2100) under SSP 370 (regional rivalry) and SSP 585 (fossil‐fuel development) scenarios. Using bias‐corrected general circulation model simulations and reconstructed terrestrial water storage (TWS) data, we quantify the contributions of atmospheric processes to HWF modulation and assess the impacts of HWF and temperature changes on water storage deficits using TWS drought severity index (TWS‐DSI) and standardized temperature index (STI). We show that Western Central Asia exhibits moisture divergence driven by significant positive thermodynamic effects, which correlates with increased HWF. In West Africa, moisture flux divergence at 1,000 hPa accounts for 45% of HWF variability, while relative humidity at 300 hPa explains 58% of HWF changes in East Asia. HWF and STI strongly influence TWS‐DSI, with high STI intensifying TWS deficits. Concurrent high HWF and wet conditions in Western North America are linked to atmospheric blocking and hydrological persistence, highlighting complex illative mechanisms. We project population exposure to HWF to rise tenfold globally by 2100, with regions such as South Asia experiencing over 100% increases due to combined climate and population effects. These findings emphasize the need for tailored adaptation strategies to mitigate heatwave impacts and ensure resilience in a warming world.

Urban planners and engineers rely on historical climate data to plan and design flood protection infrastructure that should withstand extreme flooding events with 1% annual exceedance probability (the 100-year flood). Here, we examine how hourly precipitation extremes are expected to change as temperatures rise and how this will affect urban flooding. The changes to short-duration rainfall extremes, often insufficiently considered in practice, are addressed utilizing a new temperature conditional extreme precipitation scaling approach and a novel regional climate convection-permitting model ensemble for +2 degrees C and +3 degrees C global warming scenarios. Based on hydrodynamic modeling, we estimate how future precipitation extremes translate into flood risks in two pre-alpine communes in Germany. Ignoring the impacts of climate change may lead to severe underestimations of flood risks. The +3 degrees C global warming scenario translates into an increase of 60% of affected buildings by the highest flood risk category (water level of 1 m and above). The increase in flow intensities will be greater in the commune characterized by steeper terrain. The results suggest that recently planned or implemented infrastructure projects may not be adequately equipped to cope with the anticipated effects of climate change in the coming decades.

Land-based mitigation strategies, such as afforestation and avoided deforestation, are critical to achieving the Paris Agreement's goal of limiting global warming to 1.5 degrees C or 2 degrees C. However, the biophysical impacts of anthropogenic land use and land cover change (LULCC), particularly deforestation and afforestation, on extreme weather events in West Africa remain poorly understood at the regional scale. In this study, we present the first high-resolution LULCC experiments (at 3 km resolution, covering 2012-2022) using the advanced fully coupled atmosphere-hydrology WRF-Hydro model system to assess the potential impacts of idealized land use and land management scenarios on extreme events in the West African savannah region. By analyzing 18 extreme weather indices, we show that deforestation significantly affects temperature extremes (up to 0.45 +/- 0.04 degrees C), with effects on regional rainfall extremes being approximately twice as pronounced as those on mean rainfall conditions, along with a significant increase in the number of dry days. Conversely, afforestation generally leads to increases in both mean and extreme precipitation, along with fewer dry days and shorter drought durations. Notably, afforestation produces contrasting responses in temperature extremes depending on vegetation type: converting grassland to mixed or evergreen forest reduces extreme heat via increased transpiration, while conversion to savanna or woody savanna may intensify heat extremes due to albedo-induced warming effects.

Urban planners and engineers rely on historical climate data to design flood protection infrastructure capable of withstanding extreme flooding events, typically associated with a 1% annual exceedance probability (the 100-year flood). This study examines how hourly precipitation extremes are expected to evolve with rising temperatures and how these changes will influence urban flooding risks. Specifically, we address the often-overlooked impact of short-duration rainfall extremes using a new non-stationary temperature-conditional extreme precipitation scaling method and a novel regional climate convection-permitting model ensemble for +2°C and +3°C global warming scenarios for the whole of Germany. We compare this newly generated non-stationary extreme precipitation dataset with an established dataset, and then assess the implications of the future precipitation changes on flood risks in two pre-alpine communes in Germany using hydrodynamic modeling. Our results reveal that ignoring climate change can lead to significant underestimations of flood risk. Under the +3°C scenario, flood risks increase dramatically, with a 60% rise in the number of buildings affected by high flood levels (water levels of 1 meter or more). These findings suggest that current or recently implemented flood protection infrastructure may be insufficient to address the future challenges posed by climate change, underscoring the need for adaptive planning to mitigate escalating flood risks.

The forest landscape in West Africa faces significant challenges from rapid population growth, agricultural expansion, and urbanization. These anthropogenic land-use and land-cover changes (LULCC), including deforestation and afforestation, impact ecosystem-climate-carbon cycle interactions through biogeochemical emissions and greenhouse gas uptake. However, the capacity of the land-based carbon sink, encompassing LULCC emissions and CO2 uptake, remains uncertain. This study employs the fully coupled WRF-Hydro system, incorporating surface and subsurface hydrology and a dynamic carbon cycle, to perform high-resolution (3 km) convection-permitting simulations for the period 2011-2022. It assesses regional impacts of idealized LULCC scenarios by comparing several land use and afforestation simulations representing specific land cover transitions in the Sudan savannah belt of Burkina Faso and Ghana.Model performance was validated using gross primary production (GPP) data from four eddy covariance sites along a land-use gradient (pristine savanna forest, cropland, and degraded grassland) in the Sudan savannah belt of Burkina Faso and Ghana and further evaluated by comparing simulated GPP and leaf area index (LAI) with Copernicus Land Monitoring satellite products. Overall, the model showed the best performance at the pristine savanna forest site with homogeneous vegetation.Analysing of carbon cycle variables, including GPP, NPP, NEE, carbon residence time, and soil and vegetation carbon stocks, our results reveal that deforestation reduces GPP by 60% (-1.08 ± 0.1 gC/m²), carbon stocks by 45% (-1.79 ± 0.19 kgC/m²), and carbon residence time by 25% (-3.6 ± 0.9 years). Conversely, afforestation strategies, such as converting grassland to evergreen or mixed forest, can mitigate carbon losses by significantly increasing total carbon stocks (1.6 ± 0.19 kgC/m²) through increased canopy cover. Furthermore, our results indicate that converting grassland to evergreen forest can approximately double the carbon residence time in soils and ecosystems compared to afforestation options involving woody savanna or savanna. The study also investigates the underlying physical mechanisms behind LULCC-induced terrestrial carbon cycle responses.

The West African savannas region is currently undergoing extensive agricultural intensification due to rapid population growth. Those anthropogenic land cover changes (LCC) can have significant impacts at regional and seasonal scales but also on extreme weather events to which human, natural and economical systems are highly vulnerable. However, the effects of LCC on extreme events remain either largely unexplored at regional/local scale and/or without consensus. To address this issue, we investigate the biophysical impacts of idealized land use and land management changes (LCLMCs) scenarios on climate extremes in the semi-arid West African Savannas region. This analysis is conducted using high-resolution land-cover change experiments (at 3 km) covering the period from 2011 to 2023. These experiments utilize the fully coupled WRF-Hydro system, which incorporates surface and subsurface lateral flow while describing the vegetation dynamically. The local effects of idealized LCLMCs scenarios are derived through a comparison of multiple land-use and afforestation scenario-based simulations, reflecting a specific LCC transition, occurring over the Sudan Savanna of Burkina Faso and Ghana.Analyzing 20 extreme weather indices, we find, on average, that LCC robustly lessens regional extreme rainfall by 8% for the number of wet days (R1mm) and by 7% for the heavy rainfall (R10mm) more than mean rainfall conditions (up to 2 times more). LCC can impact regional rainfall extremes 4 times more than temperature extremes on average and intensifies dry days. Afforestation options, such as the conversion of grassland to evergreen broadleaf forest or evergreen needleleaf forest, tend to mitigate the biophysical LCC-induced warming effect and lower the associated occurrence of temperature extreme events. Conversely, opposite effects can be observed under savannas-based afforestation options, likely due to their associated large sensible heat fluxes compared to grassland and cropland. The study investigates the underlying biophysical drivers behind these opposing effects.We stress here that fully coupled modeling frameworks incorporating all aspects of land-use change and local positive feedback between the terrestrial hydrological system and the overlying atmosphere are needed to better evaluate land-based mitigation and adaptation strategies.

Smallholder farming in West Africa faces various challenges, such as limited access to seeds, fertilizers, modern mechanization, and agricultural climate services. Crop productivity obtained under these conditions varies significantly from one farmer to another, making it challenging to accurately estimate crop production through crop models. This limitation has implications for the reliability of using crop models as agricultural decision-making support tools. To support decision making in agriculture, an approach combining a genetic algorithm (GA) with the crop model AquaCrop is proposed for a location-specific calibration of maize cropping. In this approach, AquaCrop is used to simulate maize crop yield while the GA is used to derive optimal parameters set at grid cell resolution from various combinations of cultivar parameters and crop management in the process of crop and management options calibration. Statistics on pairwise simulated and observed yields indicate that the coefficient of determination varies from 0.20 to 0.65, with a yield deviation ranging from 8% to 36% across Burkina Faso (BF). An analysis of the optimal parameter sets shows that regardless of the climatic zone, a base temperature of 10˚C and an upper temperature of 32˚C is observed in at least 50% of grid cells. The growing season length and the harvest index vary significantly across BF, with the highest values found in the Soudanian zone and the lowest values in the Sahelian zone. Regarding management strategies, the fertility mean rate is approximately 35%, 39%, and 49% for the Sahelian, Soudano-sahelian, and Soudanian zones, respectively. The mean weed cover is around 36%, with the Sahelian and Soudano-sahelian zones showing the highest variability. The proposed approach can be an alternative to the conventional one-size-fits-all approach commonly used for regional crop modeling. Moreover, it has the potential to explore the performance of cropping strategies to adapt to changing climate conditions.

Global warming is assumed to accelerate the global water cycle. However, quantification of the acceleration and regional analyses remain open. Accordingly, in this study, we address the fundamental hydrological question: Is the water cycle regionally accelerating/decelerating under global warming? For our investigation, we have implemented the age-weighted regional water tagging approach into the Weather Research and Forecasting (WRF) Model, namely, WRF-age, to follow the atmospheric water pathways and to derive atmospheric water residence times defined as the age of tagged water since its source. We apply a three-dimensional online budget analysis of the total, tagged, and aged atmospheric water into WRF-age to provide a prognostic equation of the atmospheric water residence times and to derive atmospheric water transit times defined as the age of tagged water since its source originating from a particular physical or dynamical process. The newly developed, physics-based WRF-age model is used to regionally downscale the reanalysis of ERA-Interim and the Max Planck Institute Earth System Model (MPI-ESM) representative concentration pathway 8.5 scenario exemplarily for an East Asian monsoon region, i.e., the Poyang Lake basin (the tagged water source area), for historical (1980-89) and future (2040-49) times. In the warmer (+1.9 degrees C for temperature and +2% for evaporation) and drier (-21% for precipitation) future, the residence time for the tagged water vapor will regionally decrease by 1.8 h (from 14.3 h) due to enhanced local evaporation contributions, but the transit time for the tagged precipitation will increase by 1.8 h (from 12.9 h) partly due to slower fallout of precipitating moisture components.

Smallholder rainfed agriculture in West Africa is vital for regional food security and livelihoods, yet it remains highly vulnerable to climate change. Persistently low crop yields, driven by high rainfall variability and frequent climate hazards, highlight the urgent need for evidence-based adaptation strategies. This study assesses the impact of climate change on maize yields in Burkina Faso (BF) using a calibrated AquaCrop model and recent climate projections. AquaCrop was calibrated using district-level maize yields from 2009 to 2022 and a genetic optimization technique. Climate change impacts were then simulated using two socioeconomic scenarios (SSP2–4.5 and SSP5–8.5) for the periods 2016–2045 and 2046–2075. Climate projections show that Burkina Faso will experience temperature increases of 0.5–3 °C and decreased precipitation, with the most severe rainfall reductions in the country’s southern half, including the crucial southwestern agricultural zone. Maize yields will predominantly decrease across the country, with projected losses reaching 20% in most regions. The southwestern agricultural zone, critical for national food production, faces substantial yield decreases of up to 40% under the SSP5-8.5 scenario. In light of these findings, future research should employ the calibrated AquaCrop model to evaluate specific combinations of adaptation strategies. These strategies include optimized planting windows, field-level water management practices, and optimal fertilizer application schedules, providing actionable guidance for smallholder farmers in West Africa.