Significance: Projections of the economic consequences of climate change are valuable for policy making but generally rely on integrated assessments that cannot account for highly localized climate effects. Most agricultural climate impact studies focus on local effects or partial productivity measures insufficient to capture national economic outcomes. Here, we directly link climate variables in specific US regions to total factor productivity (TFP). We quantify the national economic consequences of past climate variations, identify critical agricultural regions with national significance, and project future changes in TFP under different climate scenarios. We provide a physical understanding of these climate−economic links, show that the agricultural economy is becoming increasingly sensitive to climate, and lay a more concrete foundation for informed decision-making.

The CWRF is developed as a climate extension of the Weather Research and Forecasting model (WRF) by incorporating numerous improvements in the representation of physical processes and integration of external (top, surface, lateral) forcings that are crucial to climate scales, including interactions between land, atmosphere, and ocean; convection and microphysics; and cloud, aerosol, and radiation; and system consistency throughout all process modules. This extension inherits all WRF functionalities for numerical weather prediction while enhancing the capability for climate modeling. As such, CWRF can be applied seamlessly to weather forecast and climate prediction. The CWRF is built with a comprehensive ensemble of alternative parameterization schemes for each of the key physical processes, including surface (land, ocean), planetary boundary layer, cumulus (deep, shallow), microphysics, cloud, aerosol, and radiation, and their interactions. This facilitates the use of an optimized physics ensemble approach to improve weather or climate prediction along with a reliable uncertainty estimate. The CWRF also emphasizes the societal service capability to provide impactrelevant information by coupling with detailed models of terrestrial hydrology, coastal ocean, crop growth, air quality, and a recently expanded interactive water quality and ecosystem model. This study provides a general CWRF description and basic skill evaluation based on a continuous integration for the period 1979– 2009 as compared with that of WRF, using a 30-km grid spacing over a domain that includes the contiguous United States plus southern Canada and northern Mexico. In addition to advantages of greater application capability, CWRF improves performance in radiation and terrestrial hydrology over WRF and other regional models. Precipitation simulation, however, remains a challenge for all of the tested models.

Observations reveal that, in summer, westward extension of the Bermuda high enhances the Great Plains low-level jet (LLJ) that transports more moisture northward, causing precipitation increases in the Midwest and decreases in the Gulf States. Meanwhile, more warm air advection from the Gulf of Mexico to the southern Great Plains and stronger clear-sky radiative heating under high pressures over the Southeast result in warmer surface temperatures across the Gulf states. The enhanced LLJ transport of cleaner marine air from the Gulf reduces surface ozone across the southern Great Plains–Midwest. In contrast, larger transport of more polluted air from the Midwest to New England and more frequent air stagnation under high pressures in the Southeast increase ozone over most of the eastern coastal states. This Bermuda high–induced ozone change reversal between the southern Great Plains–Midwest and eastern coastal states, with a magnitude of 6 and 13.5 ppb, respectively, in summer-mean maximum daily 8-h average, exhibits strong decadal variations that should be considered in the U.S. air quality dynamic management. The observed Bermuda high signatures over the Gulf states can be well captured by regional climate and air quality models. Notable model deficiencies exist over the northern Great Plains–Midwest that are more remote to the Bermuda high and LLJ control. The regional models largely reduce these deficiencies from general circulation models (GCMs). Only 7 out of 51 GCMs can represent all key regional signatures of the Bermuda high, while none can simulate its strong association with planetary sea surface temperature anomalies. The result indicates a great challenge for GCMs to predict Bermuda high variability and change.

Hyperresolution land surface modeling provides an unprecedented opportunity to simulate locally relevant water and energy cycle, but lateral surface and/or subsurface flows that are essential at fine scale are often neglected by most one‐dimensional land surface models (LSMs). To analyze effects of lateral flows across scales, a Conjunctive Surface‐Subsurface Process model, which considers soil moisture‐surface flow interaction and quasi‐three‐dimensional subsurface flow, is implemented over a mountainous HyperHydro test bed in southwestern USA at different resolutions. Validation over more than 70 International Soil Moisture Network stations shows that there are significant improvements in soil moisture simulations from 30 km to 4 km as finer soil property and precipitation data are used, with correlation increased by 5%–16% and error decreased by 5%. Lateral surface flow has a significant influence on surface soil moisture and ground evaporation even at coarse resolution. Effect of lateral subsurface flow on soil moisture is nontrivial at 1 km or finer resolution especially over wet areas. At 100 m resolution, topography‐induced lateral subsurface flow causes drier peaks and wetter valleys, decreases latent heat by 8% at peaks, while increases it by 12% at valleys. Furthermore, influences of lateral subsurface flow on ground evaporation and vegetation transpiration are more significant during dry season due to a stronger coupling between soil moisture and evapotranspiration. Therefore, it is worthy to incorporate lateral flow processes in hyperresolution LSMs to better represent water and energy heterogeneity even with limited hyperresolution meteorological and surface data.

The community Noah land surface model with multiparameterization options (Noah‐MP) provides a plethora of model configurations with varying complexity for land surface modeling. The practical application of this model requires a basic understanding of the relative abilities of its various parameterization configurations in representing spatiotemporal variability and hydrologic connectivity. We designed an ensemble of 288 experiments from multiparameterization schemes of six physical processes to assess and reduce the structural uncertainty for land surface modeling over 10 hydrologic regions in China for the period 2001–2010. The observed latent heat (LH) was well reproduced by the ensemble. Meanwhile, most experiments underestimated sensible heat (SH) throughout the year and overestimated the cold season but underestimated the warm season terrestrial water storage anomaly (TWSA). The sensitive processes and best‐performing schemes varied not only with regions but also among variables. The LH and SH were most sensitive to runoff‐groundwater (RUN), surface heat exchange coefficient (SFC), and radiation transfer (RAD). The TWSA was dominated by RUN and RAD while largely influenced by soil moisture factor for stomatal resistance (BTR) and frozen soil permeability (INF) over some limited regions. By contrast, supercooled liquid water (FRZ) had little influence on all variables. Our optimization for individual variables produced high mean Taylor skill scores that ranged from 0.95–0.99 for LH, 0.82–0.99 for SH, and 0.63–0.95 for TWSA depending on regions. The simultaneous optimization made trade‐off among the three variables, which improved TWSA performance at the cost of reducing the skill for LH and SH over a few regions.

High‐resolution modeling became popular in recent years due to the availability of multisource observations, advances in understanding fine‐scale processes, and improvements in computing facilities. However, modeling of hydrological changes over mountainous regions is still a great challenge due to the sensitivity of highland water cycle to global warming, tightly coupled hydrothermal processes, and limited observations. Here we show a successful high‐resolution (3 km) land surface modeling over the Sanjiangyuan region located in the eastern Tibetan plateau, which is the headwater of three major Asian rivers. By developing a new version of a Conjunctive Surface‐Subsurface Process model named as CSSPv2, we increased Nash‐Sutcliffe efficiency by 62–130% for streamflow simulations due to the introduction of a storage‐based runoff generation scheme, reduced errors by up to 31% for soil moisture modeling after considering the effect of soil organic matter on porosity and hydraulic conductivity. Compared with ERA‐Interim and Global Land Data Assimilation System version 1.0 reanalysis products, CSSPv2 reduced errors by up to 30%, 69%, 92%, and 40% for soil moisture, soil temperature, evapotranspiration, and terrestrial water storage change, respectively, as evaluated against in situ and satellite observations. Moreover, CSSPv2 well captured the elevation‐dependent ground temperature warming trends and the decreased frozen dates during 1979–2014, and significant increasing trends (p < 0.05) in evapotranspiration and terrestrial water storage during 1982–2011 and 2003–2014 respectively, while ERA‐Interim and Global Land Data Assimilation System version 1.0 showed no trends or even negative trends. This study implies the necessity of developing high‐resolution land surface models in realistically representing hydrological changes over highland areas that are sentinels to climate change.