Multiscale Modeling Framework Research Articles

Impact of Tissue Damage and Hemodynamics on Restenosis Following Percutaneous Transluminal Angioplasty: A Patient-Specific Multiscale Model.

Multiscale agent-based modeling frameworks have recently emerged as promising mechanobiological models to capture the interplay between biomechanical forces, cellular behavior, and molecular pathways underlying restenosis following percutaneous transluminal angioplasty (PTA). However, their applications are mainly limited to idealized scenarios. Herein, a multiscale agent-based modeling framework for investigating restenosis following PTA in a patient-specific superficial femoral artery (SFA) is proposed. The framework replicates the 2-month arterial wall remodeling in response to the PTA-induced injury and altered hemodynamics, by combining three modules: (i) the PTA module, consisting in a finite element structural mechanics simulation of PTA, featuring anisotropic hyperelastic material models coupled with a damage formulation for fibrous soft tissue and the element deletion strategy, providing the arterial wall damage and post-intervention configuration, (ii) the hemodynamics module, quantifying the post-intervention hemodynamics through computational fluid dynamics simulations, and (iii) the tissue remodeling module, based on an agent-based model of cellular dynamics. Two scenarios were explored, considering balloon expansion diameters of 5.2 and 6.2mm. The framework captured PTA-induced arterial tissue lacerations and the post-PTA arterial wall remodeling. This remodeling process involved rapid cellular migration to the PTA-damaged regions, exacerbated cell proliferation and extracellular matrix production, resulting in lumen area reduction up to 1-month follow-up. After this initial reduction, the growth stabilized, due to the resolution of the inflammatory state and changes in hemodynamics. The similarity of the obtained results to clinical observations in treated SFAs suggests the potential of the framework for capturing patient-specific mechanobiological events occurring after PTA intervention.

Multiscale modeling for the impact behavior of 3D angle-interlock woven composites

In this paper, a multiscale modeling framework predicting the impact response of three-dimensional angle-interlock woven composites (3DAWCs) is presented. Multiscale modeling is performed sequentially at the mirco, meso, and macroscales. A microscopic representative volume element (RVE) model is established to characterize the yarn's nonlinear mechanical properties and damage behavior at different strain rates. At the mesoscale, a novel subcell-based discretization scheme is proposed to discretize 3DAWCs, and developing six representative subcells (RSCs) that bridge the microscale and macroscale effective properties. Based on it, a ballistic impact model involving six kinds of homogeneous material bricks is established. Furthermore, elastic-viscoplastic damage constitutive models are developed for the epoxy matrix and the yarn, respectively, to enhance the predicted accuracy of the multiscale model. Ballistic impact tests of 3DAWCs are conducted, and X-ray micro-computed tomography is employed to evaluate the internal damage of the 3DAWC target after impact. Validation work is conducted by comparing the experimental and numerical results of the ballistic impact of the 3DAWC target, which indicates the effectiveness and efficiency of the developed multiscale framework in the impact damage prediction of 3DAWCs.

A stochastic framework for rainfall intensity–timescale–return period relationships. Part ΙΙ: Point modelling and regionalization over Greece

In this work, we formulate a regionalization framework for rainfall intensity–timescale–return period relationships which is applied over the Greek territory. The methodology for single-site estimation is based on a stochastic framework for multi-scale modelling of rainfall intensity which is outlined in the companion paper. Five parameters are first fitted independently for each site and the resulting parameter variability is assessed. Following a systematic investigation of uncertainty and variability patterns, two parameters, i.e. the tail-index and a timescale parameter, are identified as constant in space and estimated using data pooling techniques. The other three parameters are regionalized over Greece by means of spatial interpolation and smoothing techniques that are assessed through cross-validation in a multi-model framework. The regionalization scheme is implemented in a sequential order that allows exploiting rainfall information both from rainfall stations with sub-daily resolution as well as from the higher reliability network of daily raingauges.

Micro-mechanics investigation of heterogeneous deformation fields and crack initiation driven by the local stored energy density in austenitic stainless steel welded joints

This work investigates the heterogeneous deformation and failure of HR3C austenitic stainless steel welded joints at room and typical service temperatures. Such types of welded joints are widely used in the new generation of fossil fuel power stations and are known to suffer from premature high temperature failure. Observation of in-service failures revealed that cracks may nucleate either in the heat affected zone or in the weld metal. The main objective of this work is to identify the local microstructural conditions and stress–strain fields responsible for both ductile failure and micro-crack nucleation within the weldment at low and high temperatures, respectively. To that purpose, a novel multi-scale micromechanics-based modelling framework is proposed. It relies on representative weld microstructure models digitally reconstructed from electron backscatter diffraction measurements, and dislocation density-based crystal plasticity models to describe the behaviour of individual grains in each weld region. A novel thermo-dynamically consistent relation for the configuration stored energy per unit volume is derived from the crystal plasticity framework.The single crystal models are calibrated and validated from a combination of uniaxial tensile tests on cross-weld specimens using high resolution digital image correlation techniques, representative volume elements of the polycrystals at the macro-scale, as well as micropillar compression tests at the scale of the individual grains. Predictions of heterogeneous inelastic deformation fields within the weldment at both 22 °C and 665 °C, and of micropillar compression behaviour of the individual weld single crystal phases are consistent with experimental data. The experimentally observed ductile failure of the weld metal at room temperature is successfully predicted using a Gurson-type void nucleation, growth and coalescence constitutive model. The suitability of the stored strain energy density and the accumulated plastic strain as crack initiation/creep failure indicators is investigated. It was found that predicted creep lifetimes with either indicator were very similar, and that they were relatively accurate for low stress levels.

Advanced Modeling and Optimization Strategies for Process Synthesis.

This article provides a systematic review of recent progress in optimization-based process synthesis. First, we discuss multiscale modeling frameworks featuring targeting approaches, phenomena-based modeling, unit operation-based modeling, and hybrid modeling. Next, we present the expanded scope of process synthesis objectives, highlighting the considerations of sustainability and operability to assure cost-competitive production in an increasingly dynamic market with growing environmental awareness. Then, we review advances in optimization algorithms and tools, including emerging machine learning-and quantum computing-assisted approaches. We conclude by summarizing the advances in and perspectives for process synthesis strategies.

A multiscale modeling framework for predicting strain‐dependent electrical conductivity of carbon nanotube‐incorporated nanocomposites considering the electron tunneling effect

AbstractThe present work proposes a multiscale modeling framework for predicting the strain‐dependent electrical conductivity of carbon nanotube (CNT)‐incorporated nanocomposites considering the electron tunneling effect. A micromechanical model taking into account the waviness of the CNTs is utilized and molecular dynamics simulations estimating changes in distance between CNTs in the polymer matrix are conducted in an attempt to incorporate the electron tunneling effect. A series of numerical parametric studies are carried out to examine the influence of model parameters (e.g., CNT length, number of CNT segments, and intrinsic interfacial resistivity) on the strain‐dependent electrical conductivity of the nanocomposites. In addition, CNT/polydimethylsiloxane samples are fabricated and their strain‐dependent electrical conductivity is experimentally evaluated. Finally, to verify the predictive capability of the proposed modeling framework, the present predictions are compared with experimental results.Highlights A multiscale modeling framework of CNT‐incorporated nanocomposites is proposed. A micromechanics model and molecular dynamics simulations are used. The study includes a numerical parametric investigation. The present predictions are compared with those obtained experimentally.

Development of multi-scale computational frameworks to solve fusion materials science challenges

Over the past two decades, the US-DOE has funded multiple projects that rely on high-performance computing and exascale computing platforms to accelerate scientific discoveries and address grand scientific challenges, such as harnessing fusion energy. In this article, we review in detail one of these efforts aimed at enhancing our capability to model plasma-facing materials subject to plasma and high-energy ion/neutron irradiation. The plasma surface interactions project has built a multi-scale modeling framework where many of the plasma- and high-energy ion/neutron irradiation-induced effects occurring in tungsten are explored. This knowledge is used to develop atomistically-informed, high-fidelity continuum and meso‑scale models that can be validated against experiments. We review the developments within this project, with attention to experimental validation efforts, and specifically highlight activities associated with: helium bubble bursting and equation of state, and hydrogen-helium interactions in tungsten; atomistically-informed model development for beryllium-tungsten material mixing; coupling of scrape-of-layer plasma, sheath and material models; and coupling of stochastic cluster-dynamics and crystal plasticity models to address radiation effects in tungsten under stress. Finally, we present how the project is preparing for future computational architectures, for instance through efforts to adapt atomistic methods to exascale computing.

A multiscale modeling framework for Scenario Modeling: Characterizing the heterogeneity of the COVID-19 epidemic in the US

The Scenario Modeling Hub (SMH) initiative provides projections of potential epidemic scenarios in the United States (US) by using a multi-model approach. Our contribution to the SMH is generated by a multiscale model that combines the global epidemic metapopulation modeling approach (GLEAM) with a local epidemic and mobility model of the US (LEAM-US), first introduced here. The LEAM-US model consists of 3142 subpopulations each representing a single county across the 50 US states and the District of Columbia, enabling us to project state and national trajectories of COVID-19 cases, hospitalizations, and deaths under different epidemic scenarios. The model is age-structured, and multi-strain. It integrates data on vaccine administration, human mobility, and non-pharmaceutical interventions. The model contributed to all 17 rounds of the SMH, and allows for the mechanistic characterization of the spatio-temporal heterogeneities observed during the COVID-19 pandemic. Here we describe the mathematical and computational structure of our model, and present the results concerning the emergence of the SARS-CoV-2 Alpha variant (lineage designation B.1.1.7) as a case study. Our findings show considerable spatial and temporal heterogeneity in the introduction and diffusion of the Alpha variant, both at the level of individual states and combined statistical areas, as it competes against the ancestral lineage. We discuss the key factors driving the time required for the Alpha variant to rise to dominance within a population, and quantify the impact that the emergence of the Alpha variant had on the effective reproduction number at the state level. Overall, we show that our multiscale modeling approach is able to capture the complexity and heterogeneity of the COVID-19 pandemic response in the US.

Multiscale physics-based in silico modelling of nanocarrier-assisted intravascular drug delivery

A rational design of drug nanocarriers supported by in silico modelling tools can improve the efficacy of nanosystem-based intravascular drug delivery (IVDD). Computational model development stems from the vision of replacing conventional (pre)clinical trials with advanced simulations and applies to the development of more efficient nanocarriers for intravascular therapies. To establish a standardized framework for in silico preclinical trials, it is necessary to include in silico tools that can model each experimental stage of a preclinical trial for a respective nanocarrier system and give accurate and verifiable results. This review paper highlights the status of intravascular drug delivery supported by nanocarriers and discusses the modelling stages of a physics-based multiscale modelling framework that should be developed, validated and exploited to address the need for an effective preclinical assessment of nanocarriers for IVDD.

Spatiotemporal modeling of nano-delivered chemotherapeutics for synergistic microwave ablation cancer therapy

Background and objectiveThe effectiveness of current microwave ablation (MWA) therapies is limited. Administration of thermosensitive liposomes (TSLs) which release drugs in response to heat has presented a significant potential for enhancing the efficacy of thermal ablation treatment, and the benefits of targeted drug delivery. However, a complete knowledge of the mechanobiological processes underlying the drug release process, especially the intravascular drug release mechanism and its distribution in response to MWA needs to be improved. Multiscale computational-based modeling frameworks, integrating different biophysical phenomena, have recently emerged as promising tools to decipher the mechanobiological events in combo therapies. The present study aims to develop a novel multiscale computational model of TSLs delivery following MWA implantation. MethodsDue to the complex interplay between the heating procedure and the drug concentration maps, a computational model is developed to determine the intravascular release of doxorubicin from TSL, its transvascular transport into the interstitium, transport in the interstitium, and cell uptake. Computational models can estimate the interplays among liposome and drug properties, tumor perfusion, and heating regimen to examine the impact of essential parameters and to optimize a targeted drug delivery platform. ResultsResults indicated that the synergy of TSLs with MWA allows more localized drug delivery with lower side effects. The drug release rate and tumor permeability play crucial roles in the efficacy of TSLs during MWA treatment. The computational model predicted an unencapsulated drug lime around the ablated zone, which can destroy more cancer cells compared to MWA alone by 40%. Administration of TSLs with a high release rate capacity can improve the percentage of killed cancer cells by 24%. Since the heating duration in MWA is less than 15 min, the presented combination therapy showed better performance for highly permeable tumors. ConclusionThis study highlights the potential of the proposed computational framework to address complex and realistic scenarios in cancer treatment, which can serve as the future research foundation, including advancements in nanomedicine and optimizing the pair of TSL and MWA for both preclinical and clinical studies. The present model could be as a valuable tool for patient-specific calibration of essential parameters.

Multi-scale habitat modelling of latrine site selection by sympatric carnivores in an Albany Thicket landscape, South Africa

ContextUnderstanding how species select resources often requires assessing the environment at different spatial scales. Although the function of latrines in animal communication and social interactions has been studied in several carnivores, latrine site selection remains under-researched.ObjectivesWe aimed to describe the characteristics of latrines and determine the environmental factors, operating at different scales, that drive latrine site selection by two sympatric genet species (Genetta genetta and G. tigrina) in an Albany Thicket landscape (South Africa).MethodsWe used a multi-scale modelling framework to investigate latrine site selection by comparing environmental characteristics at used latrines with that of two random points at four different scales. We then adapted a match-control design to derive the probability of latrine site selection.ResultsAt the fine scale, genets selected latrine sites located in steeper slopes and boasting a higher availability of favourable micro-structures (e.g., burrows, termite mounds, hollow branches). At the landscape scale, latrines were positively associated with riverine forest corridors where they occurred in clusters. Genets avoided open areas and high terrain ruggedness to place their latrines. The best scale-independent model revealed the higher importance of edge habitats like riverine corridors and, to a lesser extent, dirt roads.ConclusionsThis study outlines the importance of including fine scale variables in multi-scale selection studies, as they may reveal features that are crucial for animal marking behaviour. Overall, our findings highlight the scales at which various factors influence latrine site selection the most. Based on our results, we suggest management practices that maintain animal communication by preserving riverine habitats across landscapes.

Multiscale modeling of granular dynamics on flowslide triggering and runout

AbstractA hierarchical multiscale modeling framework is proposed to simulate flowslide triggering and runout. It couples a system‐scale sliding‐consolidation model (SCM) resolving hydro‐mechanical feedbacks within a flowslide with a local‐scale solver based on the discrete element method (DEM) replicating the sand deformation response in the liquefied regime. This coupling allows for the simulation of a seamless transition from solid‐ to fluid‐like behavior following liquefaction, which is controlled by the grain‐scale dynamics. To investigate the role of grain‐scale interactions, the DEM simulations replace the constitutive model within the SCM framework, enabling the capture of the emergent rate‐dependent behavior of the sand during the inertial regime of motion. For this purpose, a novel algorithm is proposed to ensure the accurate passage of the strain rate from the global analysis to the local DEM solver under both quasi‐static (pre‐triggering) and dynamic (post‐triggering) regimes of motion. Our findings demonstrate that the specifics of the coupling algorithm do not bear significant consequences to the triggering analysis, in that the grain‐scale dynamics is negligible. By contrast, major differences between the results obtained with traditional algorithms and the proposed algorithm are found for the post‐triggering stage. Specifically, the existing algorithms suffer from loss of convergence and require proper numerical treatment to capture the micro‐inertial effects arising from the post‐liquefaction particle agitation responsible for viscous‐like effects that spontaneously regulate the flowslide velocity. These findings emphasize the important role of rate‐dependent feedback for the analysis of natural hazards involving granular materials, especially for post‐failure propagation analysis.

A new open-source framework for multiscale modeling of fibrous materials on heterogeneous supercomputers

A new open-source framework for multiscale modeling of fibrous materials on heterogeneous supercomputers

Multi-scale habitat modeling framework for predicting the potential distribution of sheep gastrointestinal nematodes across Iran’s three distinct climatic zones: a MaxEnt machine-learning algorithm

Ecological niche models (ENMs) serve as valuable tools in assessing the potential species distribution, identifying crucial habitat components for species associations, and facilitating conservation efforts. The current study aimed to investigate the gastrointestinal nematodes (GINs) infection in sheep, predict and analyze their ecological niches and ranges, and identify the key bioclimatic variables influencing their distribution across three distinct climatic regions in Iran. In a cross-sectional study, a total of 2140 fecal samples were collected from semi-arid (n = 800), arid (n = 500), and humid-subtropical (n = 840) climates in East Azerbaijan, Kerman, and Guilan provinces, respectively. The flotation method was employed to assess stool samples, whereby the fecal egg count (the number of parasite eggs per gram [EPG]) was ascertained for each individual specimen. Employing a presence-only approach, the multi-scale maximum entropy (MaxEnt) method was used to model GINs' habitat suitability using 93 selected points/locations. The findings revealed that Guilan (34.2%) and East Azerbaijan (19.62%) exhibited the utmost proportion of Strongyle-type eggs. East Azerbaijan province also displayed the highest proportion of Marshallagia and Nematodirus, respectively (approximately 40% and 27%), followed by Guilan and Kerman provinces, while Kerman province had the highest proportion of Trichuris (approximately 15%). Ecological niche modeling revealed that the precipitation of the driest quarter (Bio17) exerted the most significant influence on Marshallagia, Nematodirus, Trichuris, and ُSُُُtrongyle-type eggs' presence in East Azerbaijan and Kerman provinces. For Guilan province, the most influential factor defining habitat suitability for Strongyle-type eggs, Marshallagia, and Nematodirus was increasing slope. Additionally, the distribution of Trichuris was most affected by the variable Bio2 in Guilan province. The study highlights the response of GINs to climate drivers in highly suitable regions, providing insights into ecologically favorable areas for GINs. In conclusion, this study provides a better understanding of GINs and the environmental factors influencing their transmission dynamics.

A framework for multi-scale intervention modeling: virtual cohorts, virtual clinical trials, and model-to-model comparisons

Computational models of disease progression have been constructed for a myriad of pathologies. Typically, the conceptual implementation for pathology-related in silico intervention studies has been ad hoc and similar in design to experimental studies. We introduce a multi-scale interventional design (MID) framework toward two key goals: tracking of disease dynamics from within-body to patient to population scale; and tracking impact(s) of interventions across these same spatial scales. Our MID framework prioritizes investigation of impact on individual patients within virtual pre-clinical trials, instead of replicating the design of experimental studies. We apply a MID framework to develop, organize, and analyze a cohort of virtual patients for the study of tuberculosis (TB) as an example disease. For this study, we use HostSim: our next-generation whole patient-scale computational model of individuals infected with Mycobacterium tuberculosis. HostSim captures infection within lungs by tracking multiple granulomas, together with dynamics occurring with blood and lymph node compartments, the compartments involved during pulmonary TB. We extend HostSim to include a simple drug intervention as an example of our approach and use our MID framework to quantify the impact of treatment at cellular and tissue (granuloma), patient (lungs, lymph nodes and blood), and population scales. Sensitivity analyses allow us to determine which features of virtual patients are the strongest predictors of intervention efficacy across scales. These insights allow us to identify patient-heterogeneous mechanisms that drive outcomes across scales.

A Semi-Markov Approach to Study a Group of Kinesin Motors.

We propose a general mathematical and computational approach to study cellular transport driven by a group of kinesin motors. It is a framework for multi-scale modeling that integrates kinetic models of single kinesin motors, including detachment and reattachment events, to study group behaviors of several motors. By formulating the problem as a semi-Markov process and applying a central limit theorem, asymptotic velocity and diffusivity can be readily calculated, which offers considerable computational advantage over Monte Carlo simulations in tasks such as parameter sensitivity analysis and model selection. We demonstrate the method with some examples. The importance of incorporating the intrinsic microscopic-level dynamics of individual motors is illustrated by showing how changes at the microscopic level propagate to the motor-cargo complex at a mesoscopic level. Particularly, we showcase an example in which changes in the second moment of single-motor characteristics gives rise to different first moment characteristics of the motor group.

Enhancement of fatigue life modeling using a metamodel-based global sensitivity analysis framework

Global Sensitivity Analysis (GSA) is a well-established approach to support simulation-driven design decisions where the dependency between the simulation's output and the model's input is quantifed. However, classical GSA approaches, such as Sobol’ indices based on Monte Carlo Simulations (MCS), are not convenient when computationally expensive simulation models such as Representative Volume Elements (RVE) are used as the model to analyze. A simulation framework is developed with a metamodeling-based GSA to overcome the aforementioned cost of the MCS approaches. The developed framework has been applied in a Multi-Scale Modeling (MSM) framework replacing a micromechanical RVE simulation with three different metamodels for performing GSA. The micromechanical model predicts the stiffness of a Carbon Fiber Reinforced Polymer (CFRP) material and the GSA can quantify how the experimental material parameters affect the material properties. The obtained sensitivity analysis demonstrates that void size is the most influential parameter on the outputs of interest, and the metamodel-based GSA is a computationally convenient approach.

Representation of atmosphere-induced heterogeneity in land–atmosphere interactions in E3SM–MMFv2

Abstract. In the Energy Exascale Earth System Model (E3SM) Multi-scale Modeling Framework (MMF), where parameterizations of convection and turbulence are replaced by a 2-D cloud-resolving model (CRM), there are multiple options to represent land–atmosphere interactions. Here, we propose three different coupling strategies, namely the (1) coupling of a single land surface model to the global grid (MMF), (2) coupling a single land copy directly to the embedded CRM (SFLX2CRM), and (3) coupling a single copy of land model to each column of the CRM grid (MAML). In the MAML (Multi-Atmosphere Multi-Land) framework, a land model is coupled to CRM at the CRM-grid scale by coupling an individual copy of a land model to each CRM grid. Therefore, we can represent intra-CRM heterogeneity in the land–atmosphere interaction processes. There are 5-year global simulations run using these three coupling strategies, and we find some regional differences but overall small changes with respect to whether a land model is coupled to CRM or a global atmosphere. In MAML, the spatial heterogeneity within CRM induces stronger turbulence, which leads to the changes in soil moisture, surface heat fluxes, and precipitation. However, the differences in the MAML from the other two cases are rather weak, suggesting that the impact of using MAML does not justify the increase in cost.

Evaluating Mesoscale Convective Systems Over the US in Conventional and Multiscale Modeling Framework Configurations of E3SMv1

AbstractOrganized mesoscale convective systems (MCSs) contribute a significant amount of precipitation in the Central and Eastern US during spring and summer, which impacts the availability of freshwater and flooding events. However, current global Earth system models cannot capture MCSs well and misrepresent the statistics of precipitation in the region. In this study, we investigate the representation of MCSs in three configurations of the Energy Exascale Earth System Model (E3SMv1) by tracking individual storms based on outgoing longwave radiation using a new application of TempestExtremes. Our results indicate that conventional parameterizations of convection, implemented in both low (LR; ∼150 km) and high (HR; ∼25 km) resolution configurations, fail to capture almost all MCS‐like events, in‐part because they underestimate high‐level cloud ice associated with deep convection. On the other hand, the multiscale modeling framework (MMF; cloud‐resolving models embedded in each grid‐column of ∼150 km resolution E3SMv1) configuration represents MCSs and their annual cycle better. Nevertheless, relative to observations, the E3SMv1‐MMF spatial distribution of MCSs and associated precipitation is shifted eastward, and the diurnal timing is lagged. A comparison between the large‐scale environment in E3SMv1‐MMF and ERA5 reanalysis suggests that the biases during the summer in E3SMv1‐MMF are associated with biases in low‐level humidity and meridional moisture transport within the low‐level jet. The fact that conventional parameterizations of convection, even with high‐resolution, cannot capture MCSs over the US suggests that methods with explicit representation of kilometer‐scale convective organization, such as the MMF, may be necessary for improving the simulation of these convective systems.

Evaluation of present-day extreme precipitation over the United States: an inter-comparison of convection and dynamic permitting configurations of E3SMv1

Accurate simulation of the present-day characteristics of mean and extreme precipitation at regional scales remains a challenge for Earth system models, which is due in part to deficiencies in model physics such as convective parameterization (CP), and coarse resolution. High horizontal resolution (HR, ∼25 km) and multiscale modeling framework (MMF, i.e. replacing conventional CP with embedded km-scale cloud-resolving models) are two promising directions that could help improve the interaction between subgrid-scale physical processes and large-scale climate. Here, we evaluate simulated extreme precipitation over the United States (US) across three configurations (i.e. low-resolution [LR], HR, and MMF) of the Energy Exascale Earth System Model (E3SMv1) and intercompare them against two gridded observation datasets (climate prediction center daily US precipitation and integrated multi-satellite retrievals for global precipitation measurement). We assess the model’s ability to simulate very heavy seasonal precipitation (illustrated by the difference between the 99th and 90th percentile values) as well as the spatial distributions of several extreme precipitation indices defined by the expert team on climate change detection and indices. Our results show that both the dry (i.e. consecutive dry days (CDD)) and wet (i.e. consecutive wet days, maximum 5 day precipitation, and very wet days) extremes evaluated herein show some improvement as well as degradation with MMF and HR relative to LR. These results vary across seasons and US subregions. For instance, only the very heavy precipitation of winter is improved with MMF and HR. Both configurations alleviate the well-known drizzling bias evident in LR across both winter and summer in many parts of the US, largely due to the overall improvement in intensity and frequency of precipitation. Additionally, our results suggest that while E3SMv1-MMF has higher intensity rates when it does rain, it has too many CDD during the summer, contributing to a low mean precipitation bias.