A CMIP6 LUMIP analysis of historical and projected climate impacts of land use and land cover changes in the United States

Land use and land cover changes (LULCCs) are drivers as well as outcomes of climate change, yet their impacts remain among the most uncertain aspects of climate projections. This uncertainty stems, in part, from the limited experiments focusing on LULCC-related feedbacks, compared to the plethora of established frameworks for quantifying uncertainties in the atmospheric and oceanic components of global climate models. This study investigates the effects of LULCC on climate changes in the contiguous United States by leveraging a hierarchy of simulations from the Coupled Model Intercomparison Project (CMIP6) Land Use Model Intercomparison Project (LUMIP) under various LULCCs. While local increases in cropland and grazing land have directly cooled the Midwest and northern Great Plains since the industrial era, precipitation changes are largely driven via LULCC’s interaction with the large-scale circulation. Other U.S. regions exhibit distinct LULCC impacts that are highly dependent on specific geographic and environmental conditions. Distinct and often opposing changes in regional climate are observed between projections with sustainable versus unsustainable land use. This study underscores the importance of LULCC not only in shaping regional climate projections across the U.S., but also for developing both regional and global climate adaptation strategies and policies.

1 Introduction

Land use and land cover (LULC) significantly influence land surface temperature, energy exchange, ecosystems, and water cycles (Ollinger et al., 2008; Richardson et al., 2013; Stéfanon et al., 2014; Tran et al., 2017). Human activities primarily drive land use and land cover changes (LULCC) (Foley et al., 2005), causing profound environmental shifts (Tilman et al., 2001; Klein Goldewijk et al., 2011). For instance, global ancient forests decreased by more than half from ∼55 million km² (850 CE) to ∼22 million km² (2015), while agricultural land expanded from 4.6 to 48.7 million km² (Hurtt et al., 2020). Since the 1960s, global cropland areas increased by ∼15%, significantly altering land-atmosphere interactions and affecting global climates (IPCC, 2019; Boysen et al., 2020; Al-Yaari et al., 2022; Yu and Leng, 2022).

Impacts of agricultural expansion and deforestation on surface temperatures and precipitation patterns vary regionally and seasonally. Tropical deforestation typically causes local warming and reduces precipitation by lowering evapotranspiration, weakening the cooling effect and water vapor supply provided by forests (Akkermans et al., 2014; Smith et al., 2023). In Southeast Asia, urbanization and vegetation reduction exacerbate CO₂-induced warming, altering the Indian summer monsoon (Paul et al., 2016; Gogoi et al., 2019). In mid-to high-latitudes, cropland expansion and irrigation disrupt seasonal temperature cycles and evapotranspiration (Dyer, 2011; Alkama and Cescatti, 2016; Perugini et al., 2017; Dyer and Rigby, 2020; Zhou et al., 2021).

In the United States, agricultural and forest land underwent notable changes over the last century. Cropland expanded significantly from the 18th century to the 1920s, followed by agricultural abandonment and reforestation (Kauppi et al., 2006; Woodall et al., 2016; Yu and Lu, 2018; Reidmiller et al., 2018). However, the specific impacts of these land-use changes on regional climate remain poorly understood. Addressing this gap is essential for constraining projected climate change and developing effective water and land management strategies to mitigate LULCC impacts.

With limited observational data, global climate models are critical tools for assessing LULCC impacts (Feddema et al., 2005; Seneviratne et al., 2010; Blyth et al., 2021); however, regional uncertainties persist due to inter-model variability, land-atmosphere coupling differences, and non-local feedbacks (Pitman et al., 2009; Boysen et al., 2014; 2020; Prestele et al., 2016; Blyth et al., 2021). To address these challenges, the Land Use Model Intercomparison Project (LUMIP), part of CMIP6 (Lawrence et al., 2016), was established. LUMIP offers a unified framework for model comparison, allowing land–atmosphere interactions to be simulated across models using the same experimental design, which reduces uncertainty by incorporating a wide range of model assumptions and outcomes (Al-Yaari et al., 2022; Hong et al., 2022; Zhang et al., 2024).

A synthesis of studies using LUMIP experiments highlight the significant influence of LULCC on global climate variability. While the tropics show relatively uniform temperature responses to LULCC, the extratropics display much greater variability, driven largely by non-local atmospheric feedbacks (Liu et al., 2023; Yu and Leng, 2022). Modeling results also reveal that LULCC can alter the frequency and intensity of extreme events, though with considerable variability across climate zones (Lejeune et al., 2018; Hu et al., 2019; Sy and Quesada, 2020).

However, studies focusing on North America, particularly the contiguous United States (CONUS), remain limited (Alexandru, 2018; Devanand et al., 2020). There is also less targeted research on LULCC-related uncertainties, compared to the abundance of established frameworks for quantifying uncertainties in the atmospheric and oceanic components of global climate models. From a modeling perspective, LULCC impacts on local climate occur through two pathways: (1) direct effects, which arise from interactions within the land model; and (2) indirect effects, where land-atmosphere coupling in fully coupled climate models amplifies or modulates direct changes. The relative importance of these pathways, along with associated inter-model uncertainties, remains unclear.

This study aims to address these gaps. Specifically, we leverage the land-use-forcing experiments in the CMIP6 LUMIP framework to study the impacts of LULCC on the climate of the CONUS under historical forcings and in future projections. Our findings will help improve the process-level understanding of LULCC impacts on surface energy dynamics in the United States, as well as quantify model sensitivity to potential land-use changes and management decisions, which will, in turn, facilitate climate mitigation efforts.

2 Data and methods

We evaluate CMIP6 experiments to investigate historical LULCC impacts (1850–2014) on CONUS climate, comparing 13 fully coupled historical simulations (hist) to their corresponding LUMIP experiments (hist-noLu), which differ from hist by holding LULC constant at 1850 levels (Table 1). The difference between hist and hist-noLu captures the total impact of LULCC within the coupled land-atmosphere-ocean system. Models include ACCESS-ESM1-5 (Ziehn et al., 2020), BCC-CSM2-MR (Wu et al., 2019), CESM2 (Danabasoglu et al., 2020), CMCC-ESM2 (Lovato et al., 2022), CNRM-ESM2-1 (Séférian et al., 2019), CanESM5 (Swart et al., 2019), EC-Earth3-Veg (Döscher et al., 2022), GFDL-ESM4 (Dunne et al., 2020), IPSL-CM6A-LR (Boucher et al., 2020), MIROC-ES2L (Hajima et al., 2020), MPI-ESM1-2-LR (Mauritsen et al., 2019), NorESM2-LM (Seland et al., 2020), and UKESM1-0-LL (Sellar et al., 2020); experiments and realizations are listed in Supplementary Table S1.

Table 1

Table 1. A summary of all CMIP6 experiments.

LULCC changes the climate through biophysical processes, including changes in water, energy, and carbon fluxes associated with land-use change. To understand the complex interactions between LULCC and the climate system, we identify the direct impacts of LULCC as the land surface environmental changes resulting from these local land modifications, independent of atmospheric circulation adjustments, and indirect effects as the subsequent feedbacks involving global dynamic atmospheric responses. To isolate these direct effects of LULCC forcing from the indirect atmospheric response, we use two land-only model simulations. The first, land-noLu, holds LULC constant at 1850 levels, while the second, land-hist, incorporates historical LULCC. Both simulations use the same land model configuration, including the representation of land cover, land use, and land management, as applied in the fully coupled hist simulations, with all relevant land-use features active. The land-noLu experiment mirrors land-hist but excludes any land-use changes. In both simulations, atmospheric forcing is prescribed to isolate the land-surface response rather than coupled land–atmosphere feedbacks.

Due to limited data availability, this analysis relies on five CMIP6 land-only models, with the difference between land-hist and land-noLu providing a focused assessment of the direct impact of land use forcing. The indirect impact is derived by subtracting the direct impact from the total impact. By comparing the differences between the coupled simulations (total impact) and the land-only simulations (direct impact), the influence of land–atmosphere coupling associated with LULCC over the CONUS, particularly its interaction with large-scale atmosphere–ocean circulation, can be better understood (Table 1).

We also assess how future climate conditions respond to LULCC by analyzing ten CMIP6 models under two Shared Socioeconomic Pathways: SSP126 (the sustainability pathway) and SSP370 (the regional-rivalry, high-emissions pathway). To isolate LULCC effects, we use the corresponding LUMIP sensitivity experiments SSP126-SSP370Lu and SSP370-SSP126Lu (Lawrence et al., 2016). These are summarized in Table 1. The SSP126-SSP370Lu experiment is identical to the standard SSP126 simulation except that it applies LULC from SSP370. Conversely, SSP370-SSP126Lu follows SSP370 emissions and socioeconomic conditions but uses SSP126 LULC.

These pairings allow us to examine how land-use trajectories alone influence future climate. In SSP126, land-use policies emphasize reforestation, limited cropland expansion, and broadly sustainable land management. In contrast, SSP370 reflects weak environmental regulation, extensive land conversion, and generally unsustainable land practices. Therefore, the difference between SSP126 and SSP126-SSP370Lu quantifies how a high-pressure, unsustainable land-use pathway (SSP370 LULC) affects the climate even when emissions and mitigation strategies follow the sustainable SSP126 trajectory. All LUMIP sensitivity experiments used in this study are summarized in Table 1.

LULC in LUMIP is based on the Land-Use Harmonization 2 (LUH2) dataset, a harmonized set of land-use scenarios that seamlessly integrates historical land-use reconstructions with eight future projections (Hurtt et al., 2020). The LUH2 methodology estimates fractional land-use patterns, land-use transitions, and key agricultural management details annually from 850 to 2100 AD on a 0.25-degree grid. LUH2 classifies land use into 14 categories with varying fractions of the land-use metrics in each grid-cell. For analytical clarity, these categories are aggregated into four primary groups following Arneth et al. (2017) and Zhang et al. (2024), namely, Forest, Non-Forest, Cropland, and Grazing land, each defined by summing the relevant fractional categories within the 14 LUH2 classes. The Forest category includes both primary forest (primf) and secondary forest (secdf), the latter referring to areas that have regrown after human disturbance. The Non-Forest category (non-forest nature cover) consists of non-forested primary land (primn) and secondary non-forested land (secdn). Cropland encompasses fractions of different photosynthesis-process types of crops, including C3 annual, C3 perennial, C3 nitrogen-fixing, C4 annual, and C4 perennial. Lastly, the Grazing land category comprises managed pasture (pastr) and rangeland (range).

Our analysis examines both the multi-model ensemble mean and long-term trends in monthly precipitation (PR), surface temperature (TS), and key land-surface variables, including leaf area index (LAI), land surface albedo (Albedo), 10-cm soil moisture (MRSOs), 10-m wind speed (10WS), latent heat (LH), and sensible heat (SH). All model outputs are bilinearly interpolated to a common 1 ° × 1 ° grid to enable consistent multi-model comparison. Historical climatology is defined for 1985–2014, with trends assessed over 1850–2014. Future climatology is based on the 2071–2100 mean, and future trends are computed over 2015–2100, consistent with standard CMIP6 analysis periods.

To evaluate the occurrence of extreme weather events, we use established percentile-based climate extremes indices. PR95p quantifies the annual frequency of daily precipitation above the 95th percentile; TX90p represents the percentage of days when daily maximum temperature exceeds the 90th percentile; and TN10p represents the percentage of days when daily minimum temperature falls below the 10th percentile. These indices are calculated following the IPCC AR6 methodological guidelines described by Seneviratne et al. (2021), using the 1961–1990 period to define the percentile thresholds.

To diagnose the coupled influence of LULCC on large-scale atmospheric circulation, we analyze anomalies of 250-hPa and 850-hPa streamfunction (ST250, ST850), 250-hPa velocity potential (VP250), and sea surface temperature (SST). To attribute LULCC-driven precipitation changes to underlying physical processes, we apply the vertically integrated water-vapor budget (Chou and Neelin, 2004):

$P=ET-\int \nabla ·Q-\int \partial W/\partial t+R$

Where $-\int \nabla ·Q$ is the vertically integrated horizontal divergence of water vapor, $\int \partial W/\partial t$ is the time change of change of column-integrated water vapor, and R is the residual term, capturing unresolved processes. To discriminate LULCC effects, we express the water-vapor budget in terms of differences between the LULCC and noLu experiments as follows:

$\Delta P=\Delta {ET}_{\mathit{int}}+\Delta {ET}_{land}-\int \nabla ·\left[\left(\Delta \mathit{q}\right)\overline{\mathit{V}}\right]-\int \nabla ·\left[\overline{\mathit{q}}\left(\Delta \mathit{V}\right)\right]-\int \Delta \partial W/\partial t+R$

Here, $\Delta$ denotes the difference between the LULCC and noLu experiments, and $\overline{\mathit{V}}$ and $\overline{\mathit{q}}$ are the climatological circulation and specific humidity, respectively. The total ET response is partitioned into two components: $\Delta {ET}_{\mathit{int}}$ , the land–atmosphere interaction component, captures ET changes arising from the coupled response of the atmosphere to LULCC. It is computed as (ET _hist –ET _hist-noLu) - (ET _land-hist–ET _land-noLu), and $\Delta {ET}_{land}$ , the land-only component, represents the direct ET response to prescribed LULCC in land-only simulations: ET _land-hist minus ET _land-noLu. The moisture-divergence term is similarly decomposed into thermodynamic and dynamic contributions. The thermodynamic contribution $-\int \nabla ·\left[\left(\Delta \mathit{q}\right)\overline{\mathit{V}}\right]$ , which isolates the effect of humidity changes acting under fixed circulation and the dynamic contribution $-\int \nabla ·\left[\overline{\mathit{q}}\left(\Delta \mathit{V}\right)\right]$ , which isolates circulation changes acting on climatological humidity (Seager et al., 2010).

3 Results

3.1 LULCC and trends in forest, non-forest, cropland, and grazing land

Figures 1A-1–1A-4 show the 1850 distribution of annual LULC across four land-use categories: Forest, Non-Forest, Cropland, and Grazing land. Most U.S. land then consisted of ancient Forest (Figures 1A-1) and Non-Forest areas (Figures 1A-2). Cropland covered about 30%–40% of the eastern U.S. (Figures 1A-3), while Grazing land made up 5%–10% of the central and western U.S. (Figures 1A-4). Historical trends in Figures 1B-1–1B-4 show major land-use changes from 1850 to 2014. Cropland expanded 60% in the Great Plains and Midwest, while grazing land grew 50% in the Intermountain West and 20% east of the 98th meridian and along the west coast. These shifts corresponded with a marked decline in ancient forest and non-forest lands.

Figure 1

Figure 1. The Land-Use Harmonization 2 (LUH2) land-use fractions of Forest (F) [sum of primary forest (primf) and secondary forest (secdf) fractions], Non-Forest (NF) [sum of non-forested primary land (primn) and secondary non-forested land (secdn) fractions], Cropland (CL) [sum of C3 annual, C3 perennial, C3 nitrogen-fixing, C4 annual, and C4 perennial fractions], and Grazing Land (GL) [sum of managed pasture (pastr) and rangeland (range) fractions] for (A-1–A-4) in 1850 levels, (B-1–B-4) historical linear trend from 1850 to 2014, (C-1–C-4) SSP126 linear trend from 2015 to 2100, (D-1–D-4) SSP370 linear trend from 2015 to 2100, and (E-1–E-4) linear trend difference between SSP370 and SSP126 (SSP370 minus SSP126). The red vertical lines in (A) are the locations of the 98th meridian.

Future projections under SSP126 show more forested land in the Midwest and Southeast, a slight decrease along the Southeast coast, less grazing land, and cropland expansion. (Figures 1C-1–1C-4). SSP370 projects forest land declines, cropland increases along the east coast, non-forest declines in the Intermountain West, and slight grazing land increases. (Figures 1D-1–1D-4). SSP370 and SSP126 differ significantly in forest and agricultural land changes east of the 98th meridian (Figures 1E-1–1E-4).

3.2 Impacts of historical LULCC based on coupled model simulations

The multi-model mean difference between hist and hist-noLu runs (Figure 2) shows LULCC impacts on surface energy dynamics and local climate. In summer, PR (Figure 2A) increases in the northern Great Plains and Midwest, coinciding with cropland expansion (Figures 1B-3), along with higher LH (Figure 2G) and reduced surface moisture (MRSOs; Figure 2E). Conversely, summer PR decreases in the northeast and southeast CONUS (Figure 2A), which is associated with deforestation and reforestation (Figures 1B-1) and inverse cropland trends (Figures 1B-3). This is consistent with previous findings that land-use variations affect mid-latitude precipitation differently across geographic regions (Lejeune et al., 2018; Portmann et al., 2022). Winter PR decreased across the Midwest, Northeast, and West Coast (Figure 2I), likely due to reduced LH (Figure 2O) via moisture recycling. These PR changes are associated with lower cloud cover (Luo et al., 2024), which influences radiative fluxes (Supplementary Figures S1E–S1H). These winter PR patterns, like East Coast summer PR, do not align with local LULCC (Figure 2O), indicating a role of coupling between LULCC and the large-scale atmospheric and oceanic circulation in shaping PR responses in these regions.

Figure 2

Figure 2. The long-term 13-model-ensemble mean difference of land surface environments between hist and hist-noLu experiments (hist minus hist-noLu 1985–2014) in summer (June - August) (A–H) and winter (December–February) (I–P) for (A and I) precipitation (PR), (B and J) surface temperature (TS), (C and K) leaf area index (LAI), (D and L) albedo, (E and M) 10 cm soil moisture content (MRSOs), (F and N) 10 m wind speed (10WS), (G and O) latent heat (LH), and (H and P) sensible heat (SH). Stippling denotes that over 75% of models agree with the change.

Summer TS changes show cooling anomalies over the Rocky Mountains, northern Great Plains, and Midwest (Figure 2B). East of the 98th meridian, TS decreases are associated with increased albedo (Figure 2D), 10WS (Figure 2F), and LH (Figure 2G) from cropland expansion (Figures 1B-3), consistent with the findings of Duveiller et al. (2018). In the northern Great Plains and Midwest, the cooling anomaly is tied to increased PR and cloud cover, reduced shortwave downward radiation (SWD; Supplementary Figure S1A) and SH (Figure 2H).

In contrast, cooling over the semi-arid Rocky Mountains and Intermountain West is associated with increases in leaf area index (LAI; Figure 2C) and LH (Figure 2G). These responses reflect historical grazing-land expansion (Figures 1B-4). In the CMIP6 historical simulations, LULCC often converts low-biomass non-forested vegetation to grazing land, primarily rangeland, which retains existing vegetation under LUH2 forcing (Hurtt et al., 2020; Ma et al., 2020). Many CMIP6 land surface schemes represent grazing lands as grass plant functional types (PFTs), which are parameterized with higher LAI than the low-biomass shrub PFTs they replace (Lawrence et al., 2019; Sellar et al., 2020; Amali et al., 2025). This transition increases evapotranspiration, strengthens LH, and promotes an evaporative cooling effect. Cooling magnitudes in mountainous regions, however, should be interpreted with caution. Complex land–surface interactions introduce greater inter-model variability due to GCM coarse resolutions, making the ensemble-mean cooling signal less certain (Chaney et al., 2018; Zorzetto et al., 2023).

Winter TS decreases across the CONUS, especially in the Midwest (Figure 2J), likely driven by the expansion of cropland and grazing land (Figures 1B-4), which reduces surface roughness and increases albedo (Figure 2L) and 10WS (Figure 2N). Unlike summer, winter TS decreases do not directly correspond to LH and SH changes, as the centers of maximum TS, LH, and SH anomalies are spatially shifted (Figures 2O,P). In the northern Great Plains, strong winds drive wind chill and cold air advection from the north, reducing sensible heat and lower TS. This aligns with Ge et al. (2022), who found that mid-latitude deforestation intensifies temperature advection, leading to colder winters and more extreme temperature variations. In the Midwest, Northeast, and Southeast CONUS, wind-driven temperature advection is less important and SH anomalies are weak or negative; in these regions, winter TS decreases primarily reflect increased surface albedo associated with cropland and grazing-land expansion.

Changes in soil moisture (MRSOs; Figures 2E,M) differed from other surface variables in both seasons. This is potentially because soil moisture is influenced by factors that are not solely determined by surface conditions, such as groundwater levels, land categories, and differences across land models. Although precipitation and evapotranspiration can serve as potential drivers, they do not always align due to compensating feedbacks in both arid and humid climates (McColl et al., 2017; Arsenault et al., 2018; Yang et al., 2018; Wei et al., 2008; Chatterjee et al., 2022). In addition, soil moisture is highly sensitive to land-surface type and to structural choices within models, such as plant functional type selection and parameterization schemes (Kennedy et al., 2019; Li et al., 2021). These sensitivities help explain why soil-moisture responses remain similar across seasons despite differences in other surface variables. Overall, climate patterns linked to LULCC in recent decades (Figure 1) and the seasonal anomalies from 1985 to 2014 (Figure 2) are consistent with broader trends from 1850 to 2014 (Supplementary Figure S2), highlighting the lasting impact of land-use changes.

3.3 Direct and indirect LULCC impacts based on land-only and coupled model simulations

We utilize land-only models to assess LULCC’s direct effects on land surface processes, excluding atmospheric influences. Due to the limited availability of LUMIP land-only data, this analysis is based on five models instead of the 13 models (Supplementary Table S1). We define significance when four out of five models (over 75%) agree on the sign of the change. In Figure 3, land-only simulations are compared with fully coupled simulations to separate local (direct) and remote (indirect) LULCC impacts.

Figure 3

Figure 3. The long-term 5-model ensemble mean difference of land surface environments from 1985 to 2014 for (A) coupled experiments between hist and hist-noLu in summer (June–August), (B) land-only experiments between land-hist and land-noLu in summer, (C) coupled experiments between hist and hist-noLu in winter (December–February), and (D) land-only experiments between land-hist and land-noLu in winter, including (1) surface temperature (TS), (2) leaf area index (LAI), (3) albedo, (4) 10 cm soil moisture content (MRSOs), (5) latent heat (LH), and (6) sensible heat (SH); stippling denotes that over 75% of models agree with the change. The spatial correlations between coupled and land-only experiments (s-cor) are calculated from the grid points in the U.S.

Summer TS differences between fully coupled hist and hist-noLu experiments show anomalous cooling in central and western U.S., with warming anomalies along the Gulf and Northwest Coasts (Figures 3A-1). Conversely, land-hist and land-noLu experiments show warming anomalies in the Midwest, parts of the Southeast, and along the West Coast (Figures 3B-1). While land-only simulations reproduce warm anomalies, they do not capture cold anomalies in the central and western U.S, indicating both coupled and land-only processes influence TS. In the cropland-expanding northern Great Plains and Midwest, cooling is mainly caused by indirect impacts from the interaction between LULCC and large-scale circulation, which can be found in the difference between indirect and direct impacts (Supplementary Figure S3). On the other hand, the warm anomalies in the Southeast and West Coast in coupled experiments (Figures 2B, 3A-1) are predominantly driven by local LULCC effects (Figures 3B-1), while the insignificant Midwest cooling (Figures 3A-1) is partially offset by the warming caused directly by LULCC (Figures 3B-1). These are linked to deforestation (Figures 1B-2), which reduces LH flux, contributing to higher TS (Figures 3B-5; Jin and Dickinson, 2010). In contrast, cooling in the northern Great Plains and Intermountain West in coupled experiments results from increased LH due to coupling-mediated responses to global LULCC. These positive LH anomalies are systematically stronger in the coupled simulations (Figures 3A-5) than the land-only LH changes (Figures 3B-5) and are co-located with PR anomalies (Figure 2A), likely further amplifying LH, consistent with surface water-balance reasoning (Seneviratne et al., 2010).

LULCC-induced Leaf Area Index (LAI) variations also modulate LH and TS. Land-only experiments (Figure 3B) show negative LAI anomalies east of the 98th meridian and along the West Coast (Figures 3B-2), consistent with deforestation and albedo changes (Figures 3B-3). Positive LAI anomalies west of the 98th meridian correspond to increased cropland and grazing land. In coupled experiments (Figure 3A), LAI patterns mirror land-only results (Figures 3A-2), except for the Great Plains, where positive LAI anomalies align with increased precipitation (Figures 3A-1). Soil moisture (MRSOs) differences show similar negative patterns in both coupled and land-only experiments (Figures 3A-4, 3B-4). Lower Southeast MRSOs aligns with deforestation, which can substantially impact LAI and soil water retention. Lower mid-latitude MRSOs aligns with increased cropland and grazing land, where crop irrigation impacts soil moisture and could increase evapotranspiration (Mahmood et al., 2011).

During winter, coupled simulations (Figures 3C-1) show stronger negative TS anomalies than land-only experiments (Figures 3D-1). Particularly in the north central U.S., the cooling is in part caused directly by local LULCC and enhanced via the coupled changes of global LULCC. As shown in Figures 3C-2,3 and 3D-2,3, these winter TS differences correlate with albedo and LAI changes, which are associated with the expansion of cropland and grazing land. This is unlike in summer, where TS cooling is mainly affected by increases in LH and PR that affect the surface heat balance and incoming radiation due to the combined effects of global LULCC. Along the coastal southeast, land-only experiments show positive TS anomalies while coupled simulations show negative TS anomalies, despite both showing negative LH anomalies (Figures 3C-5, 3D-5). This difference highlights the influence of albedo on winter TS changes. LH changes are similar in coupled and land-only experiments, but LH anomalies in coupled runs are larger, likely due to the local coupling between LH and PR changes (Figure 2I). In the northern Great Plains, however, pronounced cooling from the coupled LULCC versus land-only experiment suggests a significant role for wind-driven cold-air advection in winter (Ge et al., 2022), rather than reductions in SH; indeed, SH shows a positive tendency in the coupled experiment (Figures 3C-6), since a reduction in PR tends to reduce LH and consequently increase SH to maintain the surface energy balance. Overall, the land-only process strongly influences the coupled impacts of global LULCC in both season, as evidenced by the significant spatial correlation coefficients between the coupled (Figures 3A,C) and land-only (Figures 3B,D) experiments.

3.4 Impacts of LULCC on future climate projections

To assess future LULCC influences, we analyze trend differences in land surface variables across two LUMIP-SSP experiment pairs (see Data and Methods). Figure 4A shows summer trend differences between SSP370 and its counterpart, SSP370-SSP126Lu. Trends indicate drier conditions in the Northeast U.S. and wetter central Great Plains under SSP370 compared to SSP370-SSP126Lu (Figures 4A-1). These patterns are not directly linked to local LULCC differences (Figures 1E-1–1E-4), indicating the importance of large-scale circulation. TS trends increase along the East Coast (Figures 4A-2), driven by deforestation and cropland expansion. Although cropland expansion increases albedo, temperature anomalies contradict this. (Figures 4A-4). This aligns with land-only experiments (Figures 3B-3), which demonstrate that albedo is not necessarily the dominant mechanism in how LULCC affects local summer temperatures. Warming in these areas results from reduced LAI (Figures 4A-3) and LH (Figures 4A-7), linked to deforestation. Similar impacts from deforestation appear in the historical experiment (hist) over the southeastern U.S. (Figures 3A,B).

Figure 4

Figure 4. 10-model ensemble linear trend difference of land surface environments from 2015 to 2100 for (A) SSP370 coupled experiments between SSP370 and SSP126 LULCC in summer (June–August), (B) the same as (A) but for winter (December–February), (C) SSP126 coupled experiments between SS126 and SSP370 LULCC in summer, and (D) the same as (C) but for winter, including variables of (1) precipitation (PR), (2) surface temperature (TS), (3) leaf area index (LAI), (4) albedo, (5) 10 cm soil moisture content (MRSOs), (6) 10-m wind speed (10WS), (7) latent heat (LH), and (8) sensible heat (SH). The units are the changes per 85 years, and stippling denotes that over 75% of models agree with the change.

Cooling in the western Midwest (Figures 4A-2) partly reflects increased LH (Figures 4A-7). However, these changes do not match local PR trends, opposing the historical coupled results in which cooling is tied to global LULCC, increased PR and LH (Figures 2A,B,G), and minor local LULCC contributions (Figures 3B-1). In SSP370 and SSP370-SSP126Lu, decreased cropland corresponds with increased non-forested area, which elevates MRSOs (Figures 4A-5) and LH (Figures 4A-7) and lowers TS (Figures 4A-2). Across all the experiment pairs that test different land-use futures, Midwest summer TS trends are governed primarily by LH changes, while PR changes remain small and only weakly connected to TS (Figures 4A-1).

In winter, PR trends show negative anomalies across the southern Midwest and positive trends along the Gulf Coast (Figures 4B-1), which are closely tied to forest cover changes (Figures 1E-1), indicating that local forests are one of the mid-latitude atmospheric moisture sources during colder months. However, this relationship does not show in the Northeast, suggesting that the PR changes are driven by the coupled effect of global LULCC. Winter TS trends lack model consistency (no stippling, Figures 4B-2), but the pattern of higher latitude TS trends aligns with those of forest and cropland cover (Figures 1E-1, 1E-3), suggesting direct albedo effects similar to those in historical scenarios (Figures 2J,L). In the lower-latitude Southeast, TS changes are insignificant and inconsistent with PR (Figures 4B-1), unlike the historical winter cooling associated with wind speed (Figure 2J, JN), or the land-only changes of LH (Figures 3D-5) in changing TS (Figures 3D-1) in the Gulf. In addition, increases (decreases) in forested area west (east) of the Gulf Coast (Figures 1E-1) reduce (increase) albedo (Figures 4B-4) and WS10 (Figures 4B-6), which are not reflected by TS patterns, suggesting complex and uncertain LULCC impacts on TS in high-emission warming scenarios, warranting further study.

For SSP126 and SSP126-SSP370Lu, summer PR trends show positive anomalies in the northern and southern Midwest (e.g., West Virginia, Tennessee) and negative anomalies in the Southwest (Figures 4C-1). Only some positive mid-latitude PR anomalies result from local LULCC. For example, northern and southeast Midwest positive PR likely relates to cropland expansion increasing summer evapotranspiration (Figures 4C-7; Longobardi et al., 2016). Summer TS trends show cooling in the northern Midwest and Northeast and warming in the Southwest (Figures 4C-2), aligning with PR and LH trends. This suggests substantial impacts of PR and LH on TS via cloud shading and evaporative cooling (Figures 4C-1, 4C-7). The cooling in the Northeast U.S. is likely a result of forest expansion, which boosts LH and MRSOs (Figures 4C-5), consistent with historical LH-driven summer TS (Figures 4C-4) over albedo (Figures 4C-4) or WS10 (Figures 4C-6).

Winter PR trends show significant positive anomalies along the West Coast and parts of the Midwest (Figures 4D-1). These notable anomalies only partially align with forest cover changes (red shading, Figures 1E-1), indicating contributions from both the direct LULCC effects and indirect effects via interaction with the large-scale circulation. Winter TS trends in the SSP126 sensitivity experiments differ from SSP370, aligning with albedo and WS10 patterns but showing less inter-model agreement in most CONUS regions due to temperature effects from PR variability. SSP126 and SSP370 LULCC-sensitive experiments feature opposing land-use changes, with LAI (Figures 4X-3), albedo (Figures 4X-4), MRSOs (Figures 4X-5), and WS10 (Figures 4X-6) showing similar patterns with opposite signs. However, the impacts of LULCCs on PR (Figures 4X-1), TS (Figures 4X-2), LH (Figures 4X-7), and SH (Figures 4X-8) exhibit complex variations. This highlights that LULCC interactions with the global land-atmosphere-ocean system can produce regional climate responses not directly proportional to local land-use changes.

3.5 Impacts of LULCC on extreme weather

To assess how LULCC influences extreme weather, we examined changes in the frequencies of PR95p, TX90p, and TN10p between the hist and hist-noLu experiments. Figures 5A–C illustrate the summer differences between these scenarios. The northern Great Plains show an increase in PR95p events, while the Southeast exhibits a decline. Large uncertainties persist in regions such as the Midwest, where pronounced negative anomalies are observed (Figure 5A). Patterns of extremely warm (TX90p; Figure 5B) and cold (TN10p; Figure 5C) days generally follow the mean TS changes (Figure 2B). However, fewer warm events occur in the northern Great Plains and Intermountain West, and more cold events appear across the Great Plains and Midwest.

Figure 5

Figure 5. 9-model-ensemble extreme event frequency mean differences between hist and hist-noLu experiments averaged from 1985 to 2014 for (A) summer frequency of daily precipitation above the 95th percentile (PR95p), (B) the summer percentage of days when maximum temperatures exceed the 90th percentile (TX90p), and (C) the summer percentage of days when minimum temperatures fall below the 10th percentile (TN10p). (D–F) are the same as (A–C) but for the winter season. Barplot shows the spread of extreme event differences of total grid points with seven climate regions, including Northwest (NW), Southwest (SW), Northern Great Plains (NGP), Southern Great Plains (SGP), Midwest (MW), Northeast (NE), and Southeast (SE). The reference threshold is calculated from 1961 to 1990 for each experiment, and stippling denotes that over 75% of models agree with the change.

In winter, the distribution of extreme PR and TS events generally aligns with the corresponding mean state changes (Figures 2I,J), though significant model uncertainty remains (Figures 5D–F). The central and southern U.S., where LULCC shows cropland expansion, experiences notable reductions in extreme PR events. In contrast, the Northeast, with increased forest cover, shows model disagreement on PR extremes despite a decline in mean PR (Figure 5D). The winter cooling, particularly in the Midwest and northern Great Plains, corresponds with increased cropland and grazing land (Figures 1B-3, 1B-4). This cooling reduces warm events but increases extreme cold events in the southern Great Plains, Southwest, and parts of the Midwest (Figure 5F).

Under the SSP370 vs. SSP370-SSP126Lu future scenarios, summer PR95p declines in the Northeast, Midwest, southern Texas, Utah, and Arizona, but increases in the central Great Plains, Northwest, and Southwest (Figures 6A-1). These changes align with mean PR patterns (Figures 4A-1) and show strong model agreement. TX90p and TN10p also reflect the corresponding TS changes (Figures 4A-2): TX90p decreases in the Midwest, Great Plains, and along the California coast, but increases in the Southeast (Figures 6A-2), while TN10p increases across the eastern U.S. and decreases in the west (Figures 6A-3). In winter, PR95p changes (Figures 6B-1) match the mean PR response (Figures 4B-1). TX90p (Figures 6B-2) and TN10p (Figures 6B-3) in the Midwest and Southeast show patterns similar to mean TS (Figures 4B-2), though with considerable uncertainty. In other regions, extreme-event responses deviate from mean TS behavior. These inconsistencies can be connected to the types of LULCC that counterbalance the frequency of extreme events derived from mean stage changes. For example, increases in cropland in the Northeast induce fewer TN10p extremes (Figures 6B-3) even though mean TS shows cooling differences (Figures 4B-2), and in summer, mean TS shows a greater warming anomaly (Figures 4A-2) associated with reduced warm extremes (Figures 6A-2).

Figure 6

Figure 6. 10-model-ensemble extreme event frequency trend differences from 2015 to 2100 for (A) SSP370 coupled experiments between SSP370 and SSP126 LULCC in summer (June–August), (B) the same as (A) but for winter (December–February), (C) SSP126 coupled experiments between SS126 and SSP370 LULCC in summer, and (D) the same as (C) but for winter, including extreme events of (1) frequency of daily precipitation above the 95th percentile (PR95p), (2) the percentage of days when maximum temperatures exceed the 90th percentile (TX90p), and (3) the percentage of days when minimum temperatures fall below the 10th percentile (TN10p). The reference threshold is calculated from 1961 to 1990 for each experiment, and stippling denotes that over 75% of models agree with the change.

For SSP126 vs. SSP126-SSP370Lu, the PR95p patterns in summer (Figures 6C-1) and winter (Figures 6D-1) match the mean PR climatological changes (Figures 4C-1, 4D-1) but exhibit zonal variation. The variability of TX90p and TN10p in both seasons (Figures 6C-2, 6C-3, 6D-2, 6D-3) shows a moderate degree of similarity to the corresponding mean changes with significant uncertainty (Figures 4C-2, 4D-2), and the areas with the largest changes do not entirely overlap. These results demonstrate the sensitivity of extreme-event responses to regional land-surface characteristics and background climate, and emphasize the need for cautious interpretation of uncertainties, particularly in regions where model agreement is weak.

4 Discussion

4.1 The coupled impact of global LULCC

Observational and modeling studies consistently show that LULCC can modify global circulation and exert remote climate impacts (de Vrese et al., 2016; Chilukoti and Xue, 2021; Wang et al., 2021; Portmann et al., 2022; Zhang et al., 2025). In the CMIP6 framework, the difference between the coupled hist and hist-noLu experiments isolates the large-scale atmospheric response to historical LULCC through land–atmosphere–ocean interactions. Our results indicate that these global LULCC-driven large-scale circulation responses influence environmental conditions over the U.S. Figure 7 summarizes the resulting circulation anomalies for 1985–2014 in both summer and winter and provides context for the surface climate responses shown in Figures 2, 3.

Figure 7

Figure 7. The long-term 13-model-ensemble mean difference of global circulations between hist and hist-noLu experiments (hist minus hist-noLu 1985–2014) in summer (June–August) (A–D) and winter (December–February) (E–H) for (A and E) 250-hPa streamfunction (ST250), (B and F) 850-hPa streamfunction (ST850), (C and G) 250-hPa velocity potential (VP250), and (D and H) sea surface temperature (SST). Stippling denotes that over 75% of models agree with the change.

In summer, the 250-hPa streamfunction (ST250; Figure 7A) features an upper tropospheric trough over the Northwest and a high-pressure ridge over the Southeast. This dipole corresponds to enhanced precipitation in the northern Great Plains and Midwest and reduced precipitation in the Southwest (Figure 2A). The upper-level pattern resembles the summer circumglobal Rossby-wave teleconnection documented in prior work (Hoskins and Karoly, 1981; Ding et al., 2011; Wills et al., 2019; Lin et al., 2023), suggesting that LULCC can modulate it. The lower-level 850-hPa streamfunction (ST850; Figure 7B) shows a reinforcing high-pressure anomaly in the Southeast that contributes to suppressed precipitation there. The divergent component of the flow, represented by the 250-hPa velocity potential (VP250; Figure 7C), shows a weak zonal wavenumber-3 pattern with upper-level convergence over equatorial Africa, the Maritime Continent, and South America. This can be connected to changes in tropical forests, including deforestation (Hasler et al., 2009; Snyder, 2010). SST anomalies are weak and do not show clear large-scale patterns (Figure 7D), indicating that the influence of LULCC is mainly expressed through atmospheric circulation.

In winter, LULCC also produces discernible upper-level changes. The ST250 field (Figure 7E) exhibits a zonal shortwave pattern with a ridge in the western U.S. and a trough in the east, broadly consistent with the summer response and suggesting that LULCC primarily affects atmospheric internal variability (Screen and Simmonds, 2014; Schroeder et al., 2017). At 850 hPa (Figure 7F), a low-pressure anomaly in the eastern U.S. strengthens northerly flow and reduces the moisture supply from surrounding oceans, consistent with the precipitation and near-surface wind anomalies in Figures 2I,N. As in summer, the lower-level streamfunction and SST patterns (Figures 7G,H) remain weak and spatially incoherent, reinforcing the conclusion that historical LULCC affects circulation more strongly than it induces oceanic feedbacks in the coupled system.

Taken together, the hist − hist-noLu experiments reveal a detectable, though modest, influence of global LULCC on U.S. climate. By modifying large-scale atmospheric circulation in both summer and winter, historical LULCC shapes regional precipitation, temperature, and surface flux patterns—even in the absence of strong SST responses—highlighting its role as a non-negligible component of coupled climate variability.

4.2 Remote impacts and local land-atmospheric interactions

Precipitation (PR) changes associated with LULCC can arise from three pathways: (1) circulation changes produced by the global coupled response, (2) local land–atmosphere interactions driven by changes in PR patterns and land-surface conditions, and (3) land-only process changes directly tied to LULCC. In addition to circulation changes, PR anomalies can modify ET and subsequently influence local PR through precipitation recycling (Eltahir and Bras, 1996; Link et al., 2020). ET is also directly altered by LULCC in the land-only simulations (Figures 3B-5, 3D-5). To diagnose these processes within a consistent framework, we use the vertically integrated water-vapor balance to separate the contributions from land–atmosphere interaction ET (ΔET_int), land-only ET changes (ΔET_land), the thermodynamic and dynamic components of moisture-flux convergence, and the column-water tendency, as summarized in Figure 8.

Figure 8

Figure 8. The long-term 5-model ensemble mean difference of water vapor budget (WVB) between LULCC and noLu experiments ( $\left.\Delta \right)$ from 1985 to 2014 for (A) Precipitation ( $\Delta$ PR) from WVB equations in summer (June–August), (B) Evaporation from land-atmosphere interaction ( $\Delta$ ET_int) of LULCC effects calculated from [(ET _hist –ET _hist-noLu) minus (ET _land-hist–ET _land-noLu)] in summer, (C) Evaporation from land-only impacts ( $\Delta$ ET_land) of LULCC effects calculated from [ET _land-hist minus ET _land-noLu] in summer, (D) the thermodynamic component of moisture flux convergence ( $-\nabla ·\left[\left(\Delta \mathit{q}\right)\overline{\mathit{V}}\right]$ ) of LULCC effects in summer, (E) the dynamic component of moisture flux convergence ( $-\nabla ·\left[\overline{\mathit{q}}\left(\Delta \mathit{V}\right)\right]$ ) of LULCC effects in summer and (F) the column water tendency ( $\Delta \left(dW/dt\right)$ ) of LULCC effects in summer. (G–L) are similar to (A–F) but for the winter season (December–February).

In summer, the positive PR anomalies across the northern Great Plains and portions of the Midwest (Figure 8A) reflect the combined contributions of several water-vapor budget terms. First, enhanced ΔET_int (Figure 8B) provides a local positive moisture source, indicating feedbacks in which increased PR and ET amplify one another under circulation changes induced by LULCC. Second, land-only ET increases (ΔET_land; Figure 8C), especially in regions of cropland and grazing-land expansion, show that modifications to LAI, albedo, and soil moisture (Figures 1, 3) directly raise ET and add additional water vapor to the atmosphere. Third, the thermodynamic component of moisture divergence (Figure 8D) is positive over the central Great Plains, demonstrating that higher specific humidity under nearly unchanged circulation favors increased PR. Finally, the dynamic component (Figure 8E) is positive in the southern Great Plains, indicating that circulation changes associated with global LULCC enhance moisture convergence into this region. Together, these processes, remote circulation impacts, local land–atmosphere feedbacks, and direct surface-driven ET changes, act in concert to generate the summer PR increases over the Great Plains.

In contrast, the negative PR anomalies in the Southeast (Figure 8A) primarily reflect the dynamic contribution (Figure 8E), consistent with the high-pressure ridge in ST250 and ST850 (Figures 7A,B). Subsidence and anticyclonic circulation reduce moisture convergence and suppress PR, outweighing the locally positive ΔET_int signal (Figure 8B). Thus, in the Southeast, circulation-driven changes dominate over local land–atmosphere interactions, leading to a net decrease in summer PR. This spatial contrast highlights that the sign of the PR response depends on whether circulation anomalies reinforce, or counteract, local land-surface feedbacks.

In winter, the PR response to LULCC is generally weaker and consists of negative or near-neutral anomalies across much of the U.S. (Figure 8G). Land-only ET reductions (ΔET_land; Figure 8I) play an important role, particularly in regions where cropland expansion reduces ET and limits moisture availability. At the same time, the dynamic component (Figure 8K) shows negative anomalies across the eastern U.S., consistent with the ST850 trough and associated northerly flow that reduce moisture transport from the oceans (Figure 7F). The combination of circulation-driven moisture deficits and reduced ET produces negative or neutral PR anomalies in winter.

In the Great Plains, the thermodynamic contribution (Figure 8J) is positive, consistent with the upper-level trough in ST250 (Figure 7E), indicating that higher atmospheric moisture alone would support increased PR. However, this positive thermodynamic signal is partially offset by land-only ET reductions and negative dynamic contributions, resulting in a near-neutral net PR response. The winter outcome therefore reflects a partial cancellation among the moisture-budget terms.

4.3 The contributions of regional LULCC

The direct and indirect LULCC impacts shown in Figure 3 indicate that different types of land-use change (Figure 1B) modify distinct components of the surface environment, but the strength and direction of these responses depend strongly on regional geographic and climatic context. For instance, deforestation and cropland expansion across the Great Plains produce markedly different summer LH responses in the northern versus southern portions of the region (Figures 3B-5). This spatial contrast is consistent with previous studies demonstrating that the climatic effects of LULCC vary systematically with topography, land–sea contrast, and latitude (Akkermans et al., 2014; Dyer and Rigby, 2020; Zhou et al., 2021; Smith et al., 2023).

Changes in surface energy and water fluxes are also directly linked to the underlying LULCC patterns. In summer, Forest (Figures 1B-1) and Cropland (Figures 1B-3) LULCC show strong relationships with Albedo, MRSOS, and SH in the land-only experiments. These relationships can be visually identified and are confirmed by the spatial correlations (Supplementary Table S2), consistent with earlier work on local biogeophysical effects of LULCC (De Noblet-Ducoudré et al., 2012). Non-forest LULCC (Figures 1B-3) shows statistically significant correlations with TS, LAI, and LH, but the spatial patterns do not clearly align with the expected west–east dipole across the 98th meridian. This results suggests that the apparent statistical significance may be misleading, likely arising from spatial autocorrelation among neighboring grid cells which inflates the nominal significance level (Wilks, 2016), or reflecting broad regional circulation effects rather than a localized response to Non-forest land cover.

Comparison of the land-only and coupled simulations further highlights the role of interactive feedbacks in modulating these signals. As discussed in Sections 3.3 and 4.2, land-surface processes and local land–atmosphere interactions induced by LULCC are critical in shaping the coupled response. Notably, spatial correlations for variables such as LAI and LH are often higher in the coupled experiments than in the land-only runs (Supplementary Table S2). This alignment between coupled and land-only experiments suggests that local coupling mechanisms—where LULCC-driven changes in ET feed back into the atmospheric water budget and subsequently reinforce surface conditions—are a key component of the response (Duveiller et al., 2018; Santanello et al., 2018). Nevertheless, we caution that domain-wide correlations can be misleading due to spatial autocorrelation (Wilks, 2016) and the tendency for broad metrics to mask regime-dependent feedbacks (De Noblet-Ducoudré et al., 2012; Duveiller et al., 2018; Findell et al., 2017). Consequently, future assessments of LULCC impacts should prioritize sub-regional analyses, where environmental heterogeneity is minimized and local physical mechanisms can be more precisely isolated.

4.4 Uncertainty in LUMIP experiments

Our analysis of LULCC impacts over the CONUS relies on the CMIP6 LUMIP multi-model ensemble, which provides a standardized framework for isolating land-use forcing. However, these models exhibit systematic biases in key land–atmosphere processes that directly influence our results and contribute to uncertainty in LULCC impacts. Recent studies have shown that CMIP6 models struggle to reproduce realistic soil-moisture magnitude and variability, land-water storage, and soil-moisture–atmosphere coupling that strongly shape SH, LH, and PR responses over land (Qiao et al., 2022; Dong et al., 2022). Emergent-constraint studies further reveal structural errors in how models represent critical land–atmosphere interactions (Chai et al., 2025a; b, c). These limitations are particularly relevant because evapotranspiration and hydrologic processes underpin many of the LULCC-driven temperature and precipitation responses highlighted in this study.

Additional uncertainty stems from how land surface models interpret and implement prescribed land-use and land-cover changes. CMIP6 models, including those in LUMIP, use the LUH2 dataset (Hurtt et al., 2020) to specify historical and future LULCC, but each land model translates LUH2 categories (e.g., cropland, grazing land, forest, non-forest) into plant functional types, vegetation structure, management practices, irrigation, and wood-harvest schemes in different ways (Lawrence et al., 2016). In semi-arid regions such as the Rocky Mountains and Intermountain West, LUH2 often describes transitions from low-biomass non-forested lands (e.g., arid shrublands) to grazing land, even though such transitions may preserve much of the existing vegetation (Ma et al., 2020).

Some land models, such as CLM5, used in CESM2 and NorESM2, lack an explicit dynamic grazing representation and instead map grazing land to grass PFTs with higher maximum LAI than shrubs, increasing canopy density, LH, and evaporative cooling (Lawrence et al., 2019; Yu and Leng, 2022; Amali et al., 2025). Among the 13 LUMIP models examined, only three include explicit pasture and dynamic vegetation (Amali et al., 2025). In JULES (UKESM1), pastures are represented as grass within a dynamic vegetation scheme, but simulations that combine LULCC with fire disturbance lead to increased bare-soil fractions in grass-dominated western U.S. landscapes, raising albedo, reducing LH, and cooling the surface (Burton et al., 2019). These structural differences in how LULCC is implemented contribute substantially to inter-model spread in regional climate responses.

5 Summary

This study evaluates land-use and land-cover change (LULCC) impacts on regional climate across the contiguous United States (CONUS) using CMIP6 LUMIP simulations. By comparing coupled historical runs with land-only experiments in which LULC is held fixed at 1850 levels, we isolate the climate response attributable to historical LULCC. We also evaluate future changes by contrasting SSP370 simulations using SSP126 LULC and SSP126 simulations using SSP370 LULC, allowing us to quantify LULCC impacts across different socioeconomic pathways.

A central objective of this work is to distinguish locally driven LULCC effects from those mediated by large-scale atmospheric circulation. Our historical analysis reveals clear regional climate responses to LULCC. In the Midwest and northern Great Plains, cropland and grazing expansion produce pronounced summer cooling associated with increased evapotranspiration and enhanced latent heat fluxes. In winter, temperature changes stem more from altered albedo and near-surface winds linked to vegetation shifts, while precipitation-driven temperature anomalies reflect remote circulation responses to global LULCC. Extreme weather frequencies also exhibit regional sensitivities to LULCC, although uncertainty remains high due to inter-model spread.

Historical LULCC influences CONUS climate through both remote circulation changes and local land–atmosphere processes. In the coupled CMIP6 simulations, global LULCC generates modest but detectable large-scale atmospheric responses that alter moisture transport and help explain observed summer and winter precipitation changes across the U.S. At regional scales, these remote circulation effects interact with local biogeophysical responses to LULCC, including shifts in evapotranspiration, soil moisture, albedo, and surface fluxes. Over the northern Great Plains and Midwest, enhanced ET and humidity, circulation-driven moisture convergence, and land-only ET increases act together to raise summer precipitation, while in the Southeast, circulation anomalies suppress rainfall despite local ET increases. The magnitude and sign of these responses vary by land-cover type and geographic setting: cropland and grazing expansion produce strong latent-heat and LAI-driven cooling in the central U.S., whereas semi-arid regions show weaker or compensating effects. Across seasons, the combined influence of global circulation adjustments, local land–atmosphere coupling, and direct surface changes explains the spatially heterogeneous and regionally specific climate impacts of LULCC.

Future projections show distinct impacts from LULCC compared to historical experiments, driven by the sharp contrast between sustainable and unsustainable land-use pathways. Specifically, the sustainable scenario (SSP126) helps mitigate regional warming through reforestation-induced evaporative cooling, particularly in the Northeast. In contrast, the unsustainable scenario (SSP370) exacerbates warming and drying trends due to extensive deforestation and cropland expansion. Despite these clear signals, significant uncertainty remains regarding temperature variations in transitional zones between low and mid latitudes. These results indicate that future LULCC impacts on the CONUS are highly heterogeneous, underscoring the necessity for region-specific analyses.

Our findings align with prior research on LULCC impacts on midlatitude climate (Boysen et al., 2020; Yu and Leng, 2022). U.S. LULCC impacts from deforestation and cropland changes show similar influences over the Midwest and northern Great Plains, with high inter-model agreement. Historical LULCC effects along the Southeast Gulf Coast mirror those reported for tropical regions. Deforestation decreases evaporation, reduces local precipitation, and increases temperature (Akkermans et al., 2014; Smith et al., 2023); however, our results indicate LULCC-induced temperature changes in future warming scenarios are insignificant there.

Uncertainty in our results largely reflects persistent structural biases in CMIP6 LUMIP land models, including inconsistencies in soil moisture, evapotranspiration, biogeochemistry, and land–atmosphere coupling, as well as differences in how models translate LUH2 land-cover categories, particularly grazing land into plant functional types and surface fluxes (Findell et al., 2017; Lawrence et al., 2019; Gomez et al., 2025). These structural differences, along with uncertainties in coupled teleconnection and global water-balance feedbacks, contribute to the spread in simulated temperature and precipitation responses and reduce projection robustness, especially when ensemble sizes are small (Deser et al., 2012; Milinski et al., 2020).

Despite these limitations, the core qualitative features of our results, namely, the clear separation between direct and indirect LULCC impacts, the east–west contrast in regional responses across the 98th meridian, and the scenario-dependent differences emerging between SSP370- and SSP126-based land-use, remain consistent across models. Improving future assessments will require stronger integration of models with observed LULCC patterns, expanded use of regional and high-resolution modeling. Furthermore, combining LUMIP simulations with emergent constraints and bias-correction techniques could better account for systematic model errors in evapotranspiration, soil moisture, and land–atmosphere coupling (Chai et al., 2025a; b, c). Ultimately, these advancements are essential for improving the reliability and reducing the uncertainties of future climate projections under various land-use scenarios.

Data availability statement

All data associated with this research are publicly available and can be downloaded from CMIP6 data portal of USA, PCMDI/LLNL (California) at https://aims2.llnl.gov/search/cmip6/.

Author contributions

Y-HL: Software, Writing – original draft, Methodology, Formal Analysis, Visualization, Conceptualization, Investigation, Data curation, Validation, Writing – review and editing. BF: Project administration, Resources, Validation, Funding acquisition, Software, Methodology, Supervision, Writing – review and editing, Conceptualization. JH: Supervision, Methodology, Conceptualization, Writing – review and editing. JD: Supervision, Writing – review and editing, Project administration, Resources. SA: Supervision, Funding acquisition, Project administration, Writing – review and editing. CB: Data curation, Visualization, Validation, Writing – review and editing. BV: Validation, Writing – review and editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This research was supported by NSF EPSCoR Track II award OIA 2316382. JH is supported by NSF grant AGS-2047270.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2026.1699725/full#supplementary-material

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