High tide flooding time, the annual cumulative duration when local sea level exceeds flooding threshold, has increased rapidly along the U.S. East Coast in recent decades. However, quantitative contributions from different processes and the underlying drivers are not well understood. Here, we combine observational analyses with model experiments to quantify and understand the increase and acceleration of high tide flooding across both a long-term period (1950-2020) and recent decades (2001-2020), showing that the decadal sea level anomalies are as important as the sea level rising trend for the long-term flooding acceleration and are the dominant factor for the flooding increase and acceleration in recent decades. Decadal Indian Ocean Dipole remotely impacts the North Atlantic, accelerating flooding in the Mid-Atlantic Bight by weakening the Atlantic Meridional Overturning Circulation and exciting Rossby waves. The North Atlantic Oscillation accelerated flooding in the South Atlantic Bight largely through wind-driven Rossby waves. Therefore, remote forcing from the Indian Ocean, together with the North Atlantic Oscillation and global mean sea level, must be jointly considered to achieve more reliable decadal predictions of U.S. East Coast high tide flooding. Both the Indian Ocean Dipole and North Atlantic Oscillation have acted to accelerate high tide flooding along the U.S. East Coast in recent decades, according to combined observational analyses and model experiments.

Cold-season precipitation statistics in simulations from the storm-resolving WRF Model at 6-km and 1-h resolution over western North America are analyzed. Pseudo-global warming future simulations for the 2041-80 period, constrained by GCMs under the RCP8.5 scenario, are compared to the 1981-2020 historical simulation. The analysis focuses on the dynamical properties of precipitation time series at subdaily scales and on the morphology of storms. The statistical distribution of precipitation intensities in each pixel of the simulation domain is characterized through nonparametric statistical indicators: frequency of wet hours, mean wet-hour precipitation intensity, and Gini coefficient as a measure of the temporal concentration of the precipitation volume. Additionally, the temporal and spatial Fourier power spectra of precipitation time series and precipitation fields are analyzed. The half-power period (HPP) and half-power wavelength (HPW) are defined as spectral measures of the characteristic scales of precipitation's temporal and spatial patterns. The results show statistically significant increases in the mean wet-hour precipitation intensity and in the Gini coefficient in 99% of the pixels, indicating that the seasonal precipitation volume becomes more concentrated within a smaller number of hours with higher precipitation intensity. The statistics of change in the frequency of wet hours are more contrasted across the simulation domain. The changes are also reflected in the power spectra, which show the spatial and temporal variability increasing proportionally more with finer spatial and temporal scales and the HPW and HPP decreasing. These projected changes are expected to have consequences, not only in terms of hydrologic impacts but also in terms of the predictability of precipitation patterns. SIGNIFICANCE STATEMENT: The precipitation characteristics of winter storms over the western United States and southwestern Canada are analyzed in future climate simulations for the 2041-80 period. As compared to presentday climate, the most intense parts of the storms are projected to produce a higher rainfall volume, with increased concentration over smaller areas and shorter time intervals. The propensity of rainfall intensity to vary rapidly over time will be enhanced in the future according to the simulations. These model predictions imply an increased risk of rapid flooding in small basins. They also suggest that predicting several hours ahead the time and location at which a storm will produce maximum rainfall may become more challenging in the future.

Abstract While extreme precipitation is expected to increase in a warming climate, its scaling with temperature at weather timescales often produces puzzling results. Here, we focus on the summer months over the central U.S. to investigate the scaling of extreme precipitation intensity (EPI) with local temperature and determine the contribution of mesoscale convective systems (MCSs) to the EPI scaling. Using an observational data set that differentiates precipitation associated with MCS and non‐MCS storms, we find that MCS storms contribute to 70% of EPI samples at temperatures lower than 298 K where EPI increases with temperature. However, at temperatures of 298–305 K, MCSs' contribution to EPI decreases as the predominant storm type shifts from MCS to non‐MCS storms, causing EPI to decrease with temperature due to the weaker rainfall intensity associated with non‐MCS storms compared with MCS storms. Our findings underscore the important role of different storm types in affecting the EPI scaling relationships.

Mesoscale convective systems (MCSs) frequently occur over Asia during the warm season, often producing intense precipitation with associated socioeconomic impacts. Here we reveal significant trends in MCS occurrence frequency and related precipitation in Asia during the warm season (March–September) in 2001–2020, using a tracking method that combines cloud and precipitation criteria with high-resolution satellite data from the Global Precipitation Measurement mission. To examine whether there are differences between MCSs of different scales, both meso-α scales (MαCSs) and meso-β scales (MβCSs), with horizontal scales of 200–2,000 km and 20–200 km, are tracked. The distribution pattern of frequency and related precipitation of both MαCSs and MβCSs are quite similar and manifest positive trends over East Asia (EA) and Northeast Asia, and negative trend over Southeast Asia (SEA). The MCS precipitation trend contributes significantly to total precipitation trend, with MαCSs contributing the most. Our analysis indicates the trend in lower-tropospheric water vapor flux convergence has a similar spatial pattern to the MCS frequency and related precipitation trend. Based on an atmospheric moisture flux decomposition analysis, the water vapor flux convergence trend can largely be explained by the change in horizontal wind convergence, while the specific humidity trend driven largely by temperature change plays a minor role. The trend in wind convergence in EA and SEA is possibly related to the evident trend in the lower-tropospheric anticyclone over the western North Pacific and SEA, which might be due to the relatively stronger warming in the Indian Ocean during the past two decades.

Atmospheric rivers (ARs) are filamentary structures within the atmosphere that account for a substantial portion of poleward moisture transport and play an important role in Earth's hydroclimate. However, there is no one quantitative definition for what constitutes an atmospheric river, leading to uncertainty in quantifying how these systems respond to global change. This study seeks to better understand how different AR detection tools (ARDTs) respond to changes in climate states utilizing single-forcing climate model experiments under the aegis of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP). We compare a simulation with an early Holocene orbital configuration and another with CO2 levels of the Last Glacial Maximum to a preindustrial control simulation to test how the ARDTs respond to changes in seasonality and mean climate state, respectively. We find good agreement among the algorithms in the AR response to the changing orbital configuration, with a poleward shift in AR frequency that tracks seasonal poleward shifts in atmospheric water vapor and zonal winds. In the low CO2 simulation, the algorithms generally agree on the sign of AR changes, but there is substantial spread in their magnitude, indicating that mean-state changes lead to larger uncertainty. This disagreement likely arises primarily from differences between algorithms in their thresholds for water vapor and its transport used for identifying ARs. These findings warrant caution in ARDT selection for paleoclimate and climate change studies in which there is a change to the mean climate state, as ARDT selection contributes substantial uncertainty in such cases.

The Duwamish River Estuary (DRE) of Washington is prone to compound flooding during atmospheric river (AR) events. The processes contributing to such flooding (coastal and fluvial) have remained opaque to municipalities that are increasingly impacted. Here, we conduct a suite of coupled atmosphere-hydrology-ocean model simulations with varying forcing combinations (tide, surge, and/or river discharge) to identify the primary drivers of compound flooding during a recent AR event. We also test year 2,100 climate forcing to project how flooding and drivers may change in the future for the same event. We identify a clear distinction between dynamics in the downstream, engineered portion of the DRE compared to the upstream, "natural" river. Downstream, tides dominate water levels but contributions from storm surge and nonlinear tide-surge interaction elevate tide-only high waters from no flooding to major flooding. Upstream, total water levels during the event are similar to 6 cm higher than downstream due to an increasing influence of river discharge over surge and tides. Notably, nonlinear surge-river and tide-river interactions act to reduce upstream water levels up to 50% compared to estimates which linearly sum tides, surge, and river, likely reducing flood vulnerability. Under two future climate scenarios: one with only sea level rise (SLR) and another with SLR plus atmospheric warming, we find little change in mechanism contributions to water levels. Expanded flooding in both cases is largely due to SLR, as a similar to 50% increase to river discharge under the warming scenario has no impact downstream and marginally increases (similar to 3 cm) water level upstream.

Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. Consequently, the role of enhanced horizontal resolution in improved process representation in all components of the climate system continues to be of great interest. Recent simulations suggest the possibility of significant changes in both large-scale aspects of the ocean and atmospheric circulations and in the regional responses to climate change, as well as improvements in representations of small-scale processes and extremes, when resolution is enhanced. The first phase of the High-Resolution Model Intercomparison Project (HighResMIP1) was successful at producing a baseline multi-model assessment of global simulations with model grid spacings of 25–50 km in the atmosphere and 10–25 km in the ocean, a significant increase when compared to models with standard resolutions on the order of 1° that are typically used as part of the Coupled Model Intercomparison Project (CMIP) experiments. In addition to over 250 peer-reviewed manuscripts using the published HighResMIP1 datasets, the results were widely cited in the Intergovernmental Panel on Climate Change report and were the basis of a variety of derived datasets, including tracked cyclones (both tropical and extratropical), river discharge, storm surge, and impact studies. There were also suggestions from the few ocean eddy-rich coupled simulations that aspects of climate variability and change might be significantly influenced by improved process representation in such models. The compromises that HighResMIP1 made should now be revisited, given the recent major advances in modelling and computing resources. Aspects that will be reconsidered include experimental design and simulation length, complexity, and resolution. In addition, larger ensemble sizes and a wider range of future scenarios would enhance the applicability of HighResMIP. Therefore, we propose the High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) to improve and extend the previous work, to address new science questions, and to further advance our understanding of the role of horizontal resolution (and hence process representation) in state-of-the-art climate simulations. With further increases in high-performance computing resources and modelling advances, along with the ability to take full advantage of these computational resources, an enhanced investigation of the drivers and consequences of variability and change in both large- and synoptic-scale weather and climate is now possible. With the arrival of global cloud-resolving models (currently run for relatively short timescales), there is also an opportunity to improve links between such models and more traditional CMIP models, with HighResMIP providing a bridge to link understanding between these domains. HighResMIP also aims to link to other CMIP projects and international efforts such as the World Climate Research Program lighthouse activities and various digital twin initiatives. It also has the potential to be used as training and validation data for the fast-evolving machine learning climate models.

The overestimation of surface ozone concentration in low‐resolution global atmospheric chemistry and climate models has been a long‐standing issue. We first update the ozone dry deposition scheme in both high‐ (0.25°) and low‐resolution (1°) Community Earth System Model (CESM) version 1.3 runs, by adding the effects of leaf area index and correcting the sunlit and shaded fractions of stomatal resistances. With this update, 5‐year‐long summer simulations (2015–2019) using the low‐resolution CESM still exhibit substantial ozone overestimation (by 6.0–16.2 ppbv) over the U.S., Europe, eastern China, and ozone pollution hotspots. The ozone dry deposition scheme is further improved by adjusting the leaf cuticle conductance, reducing the mean ozone bias by 19%, and increasing the model resolution further reduces the ozone overestimation by 43%. We elucidate the mechanism by which model grid spacing influences simulated ozone, revealing distinctive pathways in urban versus rural areas. In rural areas, grid spacing mainly affects daytime ozone levels, where additional NO x emissions from nearby urban areas result in an ozone boost and overestimation in low‐resolution simulations. In contrast, over urban areas, daytime ozone overestimation follows a similar mechanism due to the influence of volatile organic compounds from surrounding rural areas. However, nighttime ozone overestimation is closely linked to weakened NO titration owing to the redistribution of urban NO x to rural areas. Additionally, stratosphere‐troposphere exchange may also contribute to reducing ozone bias in high‐resolution simulations, warranting further investigation. This optimized high‐resolution CESM may enhance understanding of ozone formation mechanisms, sources, and changes in a warming climate.

Global kilometer‐scale models represent the future of Earth system modeling, enabling explicit simulation of organized convective storms and their associated extreme weather. Here, we comprehensively evaluate tropical mesoscale convective system (MCS) characteristics in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non‐hydrostatic domains) simulations for both summer and winter phases. Using 10 different feature trackers applied to simulations and satellite observations, we assess MCS frequency, precipitation, and other key characteristics. Substantial differences (a factor of 2–3) arise among trackers in observed MCS frequency and their precipitation contribution, but model‐observation differences in MCS statistics are more consistent across trackers. DYAMOND models are generally skillful in simulating tropical mean MCS frequency, with multi‐model mean biases ranging from −2%–8% over land and −8%–8% over ocean (summer vs. winter). However, most DYAMOND models underestimate MCS precipitation amount (23%) and their contribution to total precipitation (17%). Biases in precipitation contributions are generally smaller over land (13%) than over ocean (21%), with moderate inter‐model variability. While models better simulate MCS diurnal cycles and cloud shield characteristics, they overestimate MCS precipitation intensity and underestimate stratiform rain contributions (up to a factor of 2), particularly over land, albeit observational uncertainties exist. Additionally, models exhibit a wide range of precipitable water in the tropics compared to reanalysis and satellite observations, with many models showing exaggerated sensitivity of MCS precipitation intensity to precipitable water. The MCS metrics developed here provide process‐oriented diagnostics to guide future model development.

The DYAMOND project (Stevens et al. 2019) provides an intercomparison framework for state-of-the-art global convection-permitting models with km-scale horizontal grid spacing that can directly simulate convective storms. We recently assessed the fidelity of the convective storms simulated by DYAMOND models using a novel feature tracking technique (Feng et al. 2023) and found a surprisingly large inter-model spread in the simulated frequency of ordinary deep convection and mesoscale convective systems (MCSs), as well as their associated precipitation. Recent works also showed that different feature tracking algorithms have significant impacts on estimating MCS characteristics including frequency, size, lifetime and precipitation (Prein et al. 2023). To further investigate how feature tracking methods affect the evaluation of global MCS simulations and our understanding of convective organization in observations and DYAMOND simulations, we are organizing a new international initiative called MCSMIP (MCS tracking Method Intercomparison Project). Preliminary results from several different feature trackers show that DYAMOND models generally underestimate observed MCS precipitation amount and their contribution to total precipitation in the tropics (Fig. 1), and the simulated MCS precipitation is too intense. However, some models have notable differences in MCS frequency and characteristics among the trackers. Potential paths towards more process-oriented model diagnostics to better understand the differences in simulated MCS and precipitation characteristics will be discussed. Figure 1. (a) Observed MCS contribution to total precipitation during DYAMOND Phase II, (b) model relative mean difference (%) from observations in the tropics. Each group of bars in (b) is from a feature tracker: PyFLEXTRKR, MOAAP, TOOCAN, tobac, TAMS, and simpleTrack, and each bar denotes a DYAMOND model. References Feng, Z. et al. (2023). Mesoscale Convective Systems in DYAMOND Global Convection-Permitting Simulations. Geophys. Res. Lett., doi: 10.1029/2022GL102603. Prein, A. et al. (2023). Km-Scale Simulations of Mesoscale Convective Systems (MCSs) Over South America – A Feature Tracker Intercomparison. DOI: 10.22541/essoar.169841723.36785590/v1.

Tropical land generally experiences the hottest period (spring) in a year just before the onset of wet season. Previous studies suggested that in a warming climate, the wet season would come later, but its origin is debated and its impact on temperature remains unknown. Here, we find that the warming of hot season would be amplified under global warming, and refer to it as "hot-season-gets-hotter" phenomenon. The amplified hot season warming is closely tied to the amplified warming of hot temperature percentiles. The hot-season-gets-hotter phenomenon is mainly due to the rainfall delay and most evident in the Amazon, where spring is warming by almost 1 K more than the annual mean and the 99th percentile temperatures are warming ~30% more than the mean by the end of 21st century in a high emission scenario. Comparing experiments with and without land-atmosphere coupling, it is further found that the rainfall delay is initially driven by the enhanced effective atmospheric heat capacity and then substantially amplified by positive soil moisture-atmosphere feedback. In the satellite period, observations consistently show that the hot-season-gets-hotter phenomenon has already emerged along with the rainfall delay in the Amazon. Intensified hot and dry spring climate can enhance risks of drought, heatwaves and wildfires, threatening the Amazon forest and habitats in the tropics.

This study assesses the representation of the observed relationship between Atlantic tropical cyclones comparison Project (HighResMIP). Most models struggle to faithfully reproduce the observed impacts of the MJO on Atlantic TCs, with the primary issue being the underestimated TC activity over the Atlantic main development region (MDR). The negative biases in genesis frequency within the MDR can be further attributed to weaker-than-observed African easterly wave (AEW) activity south of -128N. Errors in the diabatic heating profile within the Atlantic intertropical convergence zone lead to insufficient potential vorticity production in the lower troposphere and constrain the amplification of AEWs. In addition to the biased TC climatology, the eastward-propagating power of the MJO is consistently underestimated across all models. Nevertheless, in models with a higher eastward-westward power ratio, the simulated MJO demonstrates a stronger capacity to modulate subseasonal TC activity. Models with relatively realistic eastward propagation of the MJO also exhibit greater variance in tropical intraseasonal convection. Stronger contrasts in convective heating over the North Atlantic between phases 2-3 and phases 6-7 drive larger fluctuations in MDR shear and AEW activity over the Gulf of Mexico and West Africa, resulting in a more pronounced TC response to the MJO. Overall, our findings suggest that improved MDR TC climatology and MJO propagation are essential for models to accurately capture the observed modulations of Atlantic TCs by the MJO.

The present study explores the mechanism governing wintertime (November-April) precipitation over the Arabian Peninsula (AP) using a 17-yr-long (2002-18) high-resolution WRF simulation. The composite analysis of strong precipitation events suggests that the equatorward extension of the upper-level jet together with the embedded upper-level trough creates a positive (cyclonic) midlevel vorticity and subsequently generates an anomalous lower-level convergence through Ekman pumping. This leads to the development of an anomalous surface low, which is further enhanced in the presence of the existing Red Sea trough over the AP. This surface low weakens the persistent anticyclone over the AP, shifting it further eastward to the Arabian Sea. The eastward shift in the lower-level anticyclone contributes to the transport of warm, moist air from the Arabian Sea and the Red Sea toward the AP. This warm, moist air converges with the cold and dry air advected by the midlatitude jet and creates a moisture convergence zone, leading to the initiation of convection. We test the proposed mechanism through numerical experiments with modified upper-level wind and demonstrate that a strong, southward intrusion of the jet can indeed lead to precipitation over the AP. The above mechanism also explains the interannual variability of precipitation over the AP. During wet years, we notice approximately 3 m s21 stronger jet core magnitude and about a 28 equatorward shift of the jet compared to dry years. While the equatorward extension of the jet explains about 21% of the interannual variability, the jet magnitude explains around 7% of the variability during wet years.

Aerosol-cloud interactions (ACI) is a key uncertainty in our ability to forecast future climate. Robust evidences of aerosol-induced modifications to the structure and lifetime of both, rain bearing and non-rain bearing clouds has emerged from satellite observations across the globe in last two decades. These observations were also substantiated by many process-level simulation studies using weather models at cloud resolving scales in last decade. Thus, the significance of ACI at process scale on short-term meteorological perturbations is well agreed. However, the role of aerosol-cloud interactions on trends at climate scale is not evident yet. For example, if cloud occurrence is increasing over India, it is not clear if there is any substantial role of ACI in comparison to other governing factors. Here, we will present our analysis on the association of ACI with the recent trends in clouds, temperature and rainfall over India using satellite observations and global climate model simulations.First, we will discuss data analysis of simulations from CMIP5 models, to quantify the importance of ACI on extreme climate indices over Indian monsoon region. The climate models were grouped based on whether the models represent only aerosol-radiation interactions (REMADE) or the full suite of aerosol-radiation-cloud interactions (REMALL). Compared to REMADE, including all aerosol effects significantly improves the model skills in simulating the observed historical trends of all three climate indices over India. Specifically, AIE enhances dry days and reduces wet days in India in the historical period, consistent with the observed changes. However, by the middle and end of the 21st century, there is a relative decrease in dry days and an increase in wet days and precipitation intensity. Further, we will also illustrate unprecedented satellite evidences of aerosol induced positive trends in marine cloud occurrences and surface temperature during pre-monsoon over the Bay of Bengal (BOB) region. In last 15 years, increased aerosol emissions over North India have led to an increase in aerosol loading till 3 km over the BOB outflow region in monsoon onset period. The elevated aerosol loading stabilizes the lower troposphere over the region in recent years and leads the low-level cloud occurrences (below 3 km) to increase in recent years by ~20%. Incidentally, the sea surface over entire BOB is steadily warming under climate change except the pollution outflow region, suggesting potential contributing to the observed non-intuitive cooling trends in sea surface temperatures.Our findings underscore the crucial role of ACI in trends and future projections of the Indian hydroclimate and emphasizes the crucial need for improved aerosol representations in coupled models for accurate predictions of regional climate change over South Asia.

Accurate tropical cyclone (TC) track prediction is crucial for mitigating the catastrophic impacts of TCs on human life and the environment. Despite decades of research on tropical cyclone (TC) track prediction, large errors known as track forecast busts (TFBs) occur frequently, and their causes remain poorly understood. Here, we examine a few dozens of TCs using a unique TC downscaling strategy that can quantitatively assess the sensitivity of TC track on the strength of feedbacks of fine-scale clouds to environment. We show that as TFBs have a weaker environmental steering that favors scattering cumulonimbus clouds, capturing asymmetric distribution of planetary vorticity advection induced by such fine-scale clouds corrects TFBs by 60 percent. Our clear identification of such important TC track predictability source promises continuous improvement of TC track prediction as finer-scale TC clouds and their interactions with environment are better resolved as model larger-scale behaviors have improved.

Arctic sea-ice retreat has been linked to increased winter precipitation and heavy snowfall over land, likely due to a combination of enhanced evaporation from ice-free Arctic marginal seas (AMS) and changes in atmospheric circulation. However, their relative roles and contributions remain uncertain. Here, we show that a greater proportion of AMS evaporative moisture reached high-latitude land during the cold seasons from 1980-1989 to 2012-2021. Atmospheric circulation changes added an additional 13 % increase in the AMS moisture contribution, accounting for 11 % of the total increase in AMS-sourced land precipitation. Notably, 46 % of the increase in AMS-sourced extreme snowfall is attributed to circulation-driven landward moisture transport, representing an 84 % increase beyond the effect of enhanced AMS evaporation alone. Further analysis indicates that both the rise in Arctic moisture and the atmospheric circulation shifts are primarily driven by anthropogenic forcing. These findings highlight how atmospheric circulation changes amplify extreme snowfall fueled by AMS evaporation, underscoring the synergistic effects of Arctic sea ice loss and circulation change on high-latitude winter precipitation.

Abstract. Riverine dissolved organic carbon (DOC) plays a vital role in regional and global carbon cycles. However, the processes of DOC conversion from soil organic carbon (SOC) and leaching into rivers are insufficiently understood, inconsistently represented, and poorly parameterized, particularly in land surface and earth system models. As a first attempt to fill this gap, we propose a generic formula that directly connects SOC concentration with DOC concentration in headwater streams, where a single parameter, the transformation rate from SOC in the soil to DOC leaching flux, Pr, accounts for the overall processes governing SOC conversion to DOC and leaching from soils (along with runoff) into headwater streams. We then derive a high-resolution Pr map over the contiguous U.S. (CONUS) in five major steps: 1) selecting 2595 headwater catchments where observed riverine DOC data are available with reasonable quality; 2) estimating catchment-average SOC for the 2595 catchments based on high-resolution SOC data; 3) estimating the Pr values for these catchments based on the generic formula and catchment-average SOC; 4) developing a predictive model of Pr with machine learning (ML) techniques and catchment-scale climate, hydrology, geology, and other attributes; and 5) deriving a national map of Pr, based on the ML model. For evaluation, we compare the DOC concentration derived using the Pr map and the observed DOC concentration values at another 3210 headwater gauges. The resulting mean absolute scaled error and coefficient of determination are 0.73 and 0.47, respectively, suggesting the effectiveness of the overall methodology. Efforts to constrain uncertainty and evaluate the sensitivity of Pr to different factors are discussed. To illustrate the use of such a map, we derive a riverine DOC concentration reanalysis dataset for more than two million small catchments over CONUS. The map, robustly derived and empirically validated, lays a critical cornerstone for better simulating the terrestrial carbon cycle in land surface and earth system models. Our findings not only set a foundation for improving our predictive understanding of the terrestrial carbon cycle at the regional and global scales but also hold promises for informing policy decisions related to decarbonization and climate change mitigation.

Unexpected and large spring precipitation events in the Colorado River Basin (CRB) that significantly alleviated an otherwise severe water shortage have been observed for over a century, such as the "Miracle May" of 2015. Although these events are often termed as "drought-busting" or "miracle events" by water managers and the media, they have not been extensively researched or characterized. In this collaborative study with water managers across the CRB, we propose a definition for these hard-to-predict, ultra-high precipitation events occurring during the late-snow or snowmelt season. This characterization provides a framework for quantifying the frequency and intensity of extreme dry-to-wet springtime transitions. Despite limitations of climate model simulations due to uncertainties and the inhomogeneous qualities, our findings suggest that such transitions may become less frequent and less intense in a warming climate. In view of the potentially wetter but less-snowy climate in the basin, the need for future research to more quantitatively assess these "miracle events" is emphasized.

Managing water resources to meet increasing energy and food demands while maintaining environmental sustainability under climate change is a major challenge, especially when this nexus occurred in a coupled natural–human system (CNHS), where heterogeneous human activities affect the natural hydrologic cycle and vice versa. The relevant research has been limited by the lack of models that can effectively integrate human dynamics and hydrologic conditions with spatial details to examine co-evolutionary systems. To address this challenge, this paper develops a modeling framework that integrates an agent-based model (ABM; human behavior model) into a large-scale, process-based distributed hydrologic model to simulate human decisions endogenously in the hydrologic cycle. We then apply the Decision Scaling approach, an ex-post scenario analysis method, with our integrated model to study the bidirectional feedback of the CNHS under future changing climate conditions. With the Columbia River Basin (CRB) selected as the case study area, the calibration results show that the integrated model can simultaneously capture the historical irrigated water consumption and streamflow dynamics. Modeling results show that the trade-off between irrigated water consumption, hydropower generation, and streamflow will become more pronounced under hotter and wetter climate conditions at both the entire basin and regional (states and provinces) levels. Special attention should be given to “temperature thresholds” of different regions when the trade-off pattern started. The trade-off results can potentially inform the Columbia River Treaty renegotiation and provide insights for long-term water management policies.