Aim: Terrestrial biodiversity is impacted by both climate and land use change. Yet, future biodiversity projections have rarely considered these two drivers in combination. In this study, we aim to assess the individual and combined impact of future climate and land use change on global terrestrial vertebrate diversity under a 'sustainability' (SSP1-RCP2.6) and an 'inequality' (SSP4-RCP6.0) scenario. Location: Global land, excluding Antarctica. Time Period: 1995, 2080. Major Taxa Studied: Amphibians, birds, and mammals. Methods: We combined global climate-driven species distribution model (SDM) projections of 13,903 vertebrates (amphibians, birds, and mammals) with future and present land use projections from the Land Use Harmonisation 2 (LUH2) project. We refined the SDM outputs by the habitat requirements of each species using a land use filtering approach. We then analyzed future species richness changes globally, per region, and per land use category, and looked at taxon-specific effects. Results: Under both scenarios, decreases in future species richness dominate at low and mid-latitudes, with climate and land use change playing an equally important role. Land use change can be either an alleviating (SSP1-RCP2.6) or an exacerbating (SSP4-RCP6.0) factor of climate-induced biodiversity loss. Sub-Saharan Africa is projected to become a high-risk area for future land use-driven biodiversity loss under the SSP4-RCP6.0. Under SSP1-RCP2.6, forested and non-forested land areas increase, while SSP4-RCP6.0 leads to higher rates of deforestation and pasture expansion. Mammals experience the largest climate-driven losses, affecting 56.4% of land area under SSP4-RCP6.0, while amphibians are particularly vulnerable to land use-driven losses, especially under SSP4-RCP6.0. Main Conclusions: Our results suggest that both climate and land use pressures on biodiversity will be highest in lower latitudes, which harbor the highest levels of biodiversity.

Land use management can help address the human-induced climate and biodiversity crises. However, substantial transformations in land systems are needed to meet internationally agreed targets concerning nature conservation, restoration, sustainable agriculture, and tree cover. Such transformations influence land-atmosphere exchanges of energy, water, and carbon, and could have particularly strong effects on local to regional climate through changes in albedo and evapotranspiration. Here, we explore how land use management in Europe, consistent with the Kunming-Montreal Global Biodiversity Framework, the Nature Futures Framework, and a sustainable low-emissions scenario, would affect the European climate mid-century. Using Earth System Modelling and detailed land use, habitat, and species projections, we show that policy implementation guided by relational values (Nature as Culture) could lead to additional warming and drying further threatening biodiversity and human well-being. Conversely, promoting intrinsic values (Nature for Nature) or ecosystem services (Nature for Society) would not add major challenges for climate adaptation and mitigation. These different outcomes highlight the need to develop integrative land use scenarios that enhance biodiversity and stabilise the climate, while considering feedbacks from land to the atmosphere. Such scenarios could help navigate trade-offs and inform policy implementation in Europe.

Forests play a key role in the global commitments to reach carbon neutrality in the coming decades. Global maps of potential tree cover at high spatial resolution for current and future climate scenarios are needed to assess the risk of future forest carbon loss and carbon storage potential through afforestation/reforestation projects. Here, we present data integrating satellite-based tree cover observations into a machine learning framework to estimate tree cover carrying capacity (percentage of tree coverage), which reflects the maximum potential tree cover, accounting for natural disturbances. Our model improves upon previous estimates by reducing prediction errors, better aligning with tree cover observations in intact areas, and lowering spatial variance in areas without topographical variation. However, uncertainties remain, particularly in regions where human activity has significantly altered landscapes. The tree cover carrying capacity provides an estimate of potential tree cover based on climatic and soil conditions. This serves as an initial step in identifying afforestation/reforestation opportunities but should be further assessed for land-use competition, ecological feasibility, and other limitations.

Anthropogenic land-use change (LUC) substantially impacts climate dynamics, primarily through modifications in the surface biogeophysical (BGP) and biogeochemical (BGC) fluxes, which alter the exchange of energy, water, and carbon with the atmosphere. Despite the established significance of both the BGP and BGC effects, their relative contribution to climate change remains poorly quantified. In this study, we leveraged data from an unprecedented number of Earth system models (ESMs) of the latest generation that contributed to the Land Use Model Intercomparison Project (LUMIP), under the auspices of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our analysis of BGP effects indicates a range of global annual near-surface air temperature changes across ESMs due to historical LUC, from a cooling of −0.23 °C to a warming of 0.14 °C, with a multi-model mean and spread of -0.03±0.10 °C under present-day conditions relative to the pre-industrial era. Notably, the BGP effects indicate warming at high latitudes. Still, there is a discernible cooling pattern between 30° N and 60° N, extending across large landmasses from the Great Plains of North America to the Northeast Plain of Asia. The BGC effect shows substantial land carbon losses, amounting to -127±94 Gt C over the historical period, with decreased vegetation carbon pools driving the losses in nearly all analysed ESMs. Based on the transient climate response to cumulative emissions (TCRE), we estimate that LUC-induced carbon emissions result in a warming of approximately 0.21±0.14 °C, which is consistent with previous estimates. When the BGP and BGC effects are taken together, our results suggest that the net effect of LUC on historical climate change has been to warm the climate. To understand the regional drivers (and thus potential levers to alter the climate), we show the contribution of each grid cell to LUC-induced global temperature change, as a warming contribution over the tropics and subtropics with a nuanced cooling contribution over the mid-latitudes. Our findings indicate that, historically, the BGC temperature effects dominate the BGP temperature effects at the global scale. However, they also reveal substantial discrepancies across models in the magnitude, directional impact, and regional specificity of LUC impacts on global temperature and land carbon dynamics. This underscores the need for further improvement and refinement in model simulations, including the consideration and implementation of land-use data and model-specific parameterizations, to achieve more accurate and robust estimates of the climate effect of LUC.

Forests play an important role in regulating land-atmosphere interactions, e.g., the temperature difference (δT) between land surface and air. However, previous studies have primarily focused on analyzing spatial characteristics of δT at global or regional scales, with limited research on its diurnal variations especially using observational data. In this research, we investigated the diurnal changes of δT, using air temperature from large numbers of meteorological stations and surface temperature data from ERA5-Land, for forested areas in China. Results revealed that the diurnal variations in δT (2.18°C) were greater than the observation across seasons (0.8°C), highlighting the importance of considering diurnal scale in understanding δT dynamics. The hourly δT exhibited strong positive correlations with Bowen ratio albeit with a 2-3 hour time lag. Obvious relations also detected between δT and precipitation at daytime, while nighttime relationships remained uncertain when considering the influence of elevation. Simulations from Community Earth System Model (CESM) agree well with the δT-precipitation relations during the daytime, but it overemphasizes the role of elevation in controlling hydrothermal process. We remain hopeful for further enhancement of relevant physical processes in CESM can improve its ability in simulating such interactions.

With global warming, increased heat stress will substantially impact the rural labor force. Understanding and quantifying this impact is difficult, especially due to regional differences: Does the temperature increase? Is there more solar exposure? Does humidity respond non-linearly with respect to temperature changes? Furthermore, humans are resourceful, and local environments could provide adaptation methods to decrease heat impacts. A policy-relevant assessment in the context of the Paris Agreement is even more difficult with existing CMIP-type simulations with prescribed greenhouse gas trajectories that lead to a different and often non-stable warming for each model. To resolve the impacts climate mitigation and adaptation on heat stress on warming levels with specific relevance for the Paris Agreement, we use the Community Earth System Model (CESM2) driven by emissions from the Adaptive Emissions Reduction Approach (AERA) to generate climate mitigation scenarios stabilized at 1.5°C, 2.0°C and 3.0°C of global warming. One form of adaptation to heat stress impacts is to use the local environment for cooling. Within CESM2, we compare the direct and indirect exposure to solar radiation within the vegetated canopy as an inexpensive form adaptation. To diagnose the heat stress conditions we use the International Organization for Standardization (ISO) 7243, the Wet Bulb Globe Temperature (WBGT), realized by first principles representation of the globe, dry bulb, and natural wet bulb thermometers utilizing CESM2’s temperature, humidity, winds, and radiation. The WBGT values are transformed into labor capacity using standardized algorithms (e.g. NIOSH or Lancet) and the above canopy (no adaptation) and below canopy (with adaptation) labor capacity are directly compared to each other. We show that the potential to adapt by using the local environment for cooling is not uniform across regions. For example, evaluating the hottest seasonal period (defined as a local summer), at the 3.0°C mitigation scenario in equatorial Southeast Asia, adaptation can save up to 50% of total labor capacity losses. However, in northern South Asia, adaptation saves only 10% of the seasonal labor capacity losses. These results demonstrate that rural laborers in some locations may have limited capacity to adapt to differing global mitigation strategies and may require mechanical cooling or other expensive forms of adaptation.

Increasing conservation efforts are required to avert biodiversity decline caused by climate and land use changes. In a recent study (Hari et al. in prep), we combined climate change scenarios (RCP2.6 and RCP6.0) and land use change projections to assess their impact on future species distribution for a large number of mammals, birds and amphibians. Future projections of land use change were derived from the Land Use Harmonization dataset v2 (LUH2), which does not make any explicit assumptions about the area under protection in these scenarios. Here, we extend the scope of our future biodiversity projections by adding new land use scenarios explicitly accounting for different “Nature Futures” in the sense of different levels of biodiversity conservation (i.e., current protected areas or 30x30 target). In the first conservation scenario, we fix the protected areas based on the World Database on Protected Areas (WDPA), thereby assuming that protected areas will remain the same in the future as it is today. In a second category of scenarios, we create land use scenarios compatible with the Global Biodiversity Framework’s “30x30” target based on the spatially optimized dataset by Jung et al. (2021) combined with LUH2. We then quantify how incorporating different levels of protected areas for conservation change the future species richness based on our land use filtering approach. We also analyze how these two scenarios of land management for conservation interfere with different levels of global warming and what are the implications for the climate resilience of different biodiversity conservation choices.

The forest understory generally experiences temperature variations that are dampened compared to adjacent open areas (known as the “buffering effect”), allowing the development of a forest microclimate and associated ecological conditions. It is however unclear to what extent forests will maintain this buffering effect under increasing global warming. Providing reliable projections of future forest microclimates is therefore crucial to anticipate climate change impacts on forest biodiversity, and to identify corresponding conservation strategies. Recent empirical studies suggest that the buffering of air temperature extremes in forest understory compared to open land could increase with global warming, albeit at a slower rate than macroclimate temperatures. Here, we investigate the trend of this temperature buffering effect in a high-emission global warming scenario, using the process-based Land Surface Model CLM5.1. We find biome-dependant buffering trends with strongest values in tropical forests where buffering increases for every degree of global warming by 0.1 °C for maximum soil temperature, and by 0.2 °C for maximum canopy air temperature. In boreal regions, forest microclimate exhibits a strong seasonality and the effect of global warming on forest understory is less clear. This first Land Surface Model assessment of future forest microclimate highlights the specific importance of tropical forest canopies in particular, in maintaining hospitable conditions for understory species while also increasing their climate debt under global warming. Our research also illustrates the potential and limitations of Land Surface Models to simulate forest microclimate, and calls for further collaborations between Earth system modelers and ecologists to jointly question climate and biosphere dynamics.

Soil carbon sequestration is an important strategy for climate change mitigation. Soil carbon stocks on agricultural lands can be augmented through sustainable land management practices such as organic manures addition, cover cropping, mulching, conservation tillage and agroforestry. Soil carbon sequestration has several co-benefits, of which increased water holding capacity and infiltration are often named. However, a global scale quantification of these hydrological co-benefits for water availability is still lacking. In this study, we aim to quantify how soil carbon sequestration impacts soil water budget and availability, to identify potential hydrological co-benefits. We use the Community Land Model (CLM) version 5.2 in land-only mode with prescribed phenology to conduct idealized experiments simulating present-day climate conditions with altered soil carbon stocks after 20 years of sequestration. Three scenarios of carbon sequestration are investigated, based on spatially explicit soil organic carbon input maps. These include two scenarios with high and medium sequestration rates focused on cropland. Additionally, an aspirational scenario with a 0.4% annual increase in soil organic carbon stocks is conducted, which follows the "4 per mille" initiative target. Upon analyzing the simulations at subgrid level for the crop fraction of the grid cell, our findings indicate that, overall, soil carbon sequestration enhances the water holding capacity by increasing the field capacity and reducing the permanent wilting point of the soil. This increase in water holding capacity predominantly arises from augmented porosity, which in turn surpasses the rise in actual water content. Consequently, the saturated fraction of soils across most regions decreases. Furthermore, CLM simulations consistently demonstrate that elevated carbon stocks reduce the surface runoff and subsurface drainage, and increase soil evaporation. The upper soil layers, corresponding to the layers with elevated soil carbon, exhibit increased water content, whereas lower layers indicate either negligible or slight water content reduction. This is particularly accentuated in arid regions, which leading to an overall decline in water content in these areas. Finally, water stress is found to be decreasing, which indicates improved water retention in carbon-sequestered soil and enhances the soil sponginess. Overall, despite remaining modelling uncertainties, particularly linked to soil hydrological parametrizations and their dependency on soil carbon fractions, these sensitivity experiments reveal the potential of carbon sequestration to increase water availability and counteract water scarcity.

AbstractTo achieve the 1.5°C target of the Paris agreement, rapid, sustained, and deep emission reductions are required, which often includes negative emissions through land‐based mitigation. However, the effects of future land‐use change on climate are often not considered when quantifying the climate‐induced impacts on human heat stress and labor capacity. By conducting simulations with three fully coupled Earth System Models, we project the effects of land‐use change on heat stress and outdoor labor capacity for two contrasting future land‐use scenarios under high‐ambition mitigation. Achieving a sustainable land‐use scenario with increasing global forest cover instead of an inequality scenario with decreasing forest cover in the Global South causes a global cooling ranging between 0.09°C and 0.35°C across the Earth System Models. However, the effects on human heat stress are less strong, especially over the regions of intense land‐use change such as the tropics, where biogeophysical effects on near‐surface specific humidity and wind speed counteract the cooling effect under warm extremes. The corresponding influence on outdoor labor capacity is small and inconsistent across the three Earth System Models. These results clearly highlight the importance of land‐use change scenarios for achieving global temperature targets while questioning the adaptation potential for reduction in heat stress.

The forest understory experiences temperature variations that are dampened compared to adjacent open areas, allowing the development of a forest microclimate and associated ecological conditions. It is however unclear to what extent forests will maintain this buffering effect under increasing global warming. Providing reliable projections of future forest microclimates is therefore crucial to anticipate climate change impacts on forest biodiversity, and to identify corresponding conservation strategies. Recent empirical studies suggest that the buffering of air temperature extremes in forest understory compared to open land could increase with global warming, albeit at a slower rate than macroclimate temperatures. Here, we investigate the trend of this temperature buffering effect in a high-emission global warming scenario, using the process-based Land Surface Model CLM5.1. We find biome-dependant buffering trends with strongest values in tropical forests where buffering increases for every degree of global warming by 0.1 circle C for maximum soil temperature, and by 0.2 circle C for maximum canopy air temperature. In boreal regions, forest microclimate exhibits a strong seasonality and the effect of global warming is more uncertain. Thus, our results highlight the importance of tropical forest canopies in particular, in maintaining hospitable conditions for understory species while increasing their climate debt under global warming. Our research also illustrates the potential and limitations of Land Surface Models to simulate forest microclimate, and calls for further collaborations between Earth system modelers and ecologists to jointly question climate and biosphere dynamics.

The international community, through treaties such as the Paris agreement, aims to limit climate change to well below 2 degrees C, which implies reaching carbon neutrality around the second half of the century. In the current calculations underpinning the various roadmaps toward carbon neutrality, a major component is a steady or even expanding terrestrial carbon sink, supported by an increase of global forest biomass. However, recent research has challenged this view. Here we developed a framework that assesses the potential global equilibrium of forest biomass under different climate change scenarios. Results show that under global warming carbon storage potential in forest aboveground biomass gradually shifts to higher latitudes and the intensity of the disturbance regimes increases significantly almost everywhere. CO2 fertilization stands out as the most uncertain process, with different methods of estimation leading to diverging results by almost 155 PgC of above ground biomass at equilibrium. Overall, assuming that the sum of human pressures (e.g. wood extraction) does not change over time, that total forest cover does not change significantly and that the trend in CO2 fertilisation as it is currently estimated from satellite proxy observations remains, results show that we have reached (or are very close to reaching) the peak of global forest carbon storage. In the short term, where increased disturbance regimes are assumed to act quicker than increased forest growth potential, global forests might instead act as a carbon source, that will require even more effort in decarbonization than previously estimated. Therefore, the potential of forests as a nature-based solution to mitigate climate change brings higher uncertainties and risks than previously thought.

Forest regeneration is a crucial strategy for mitigating and adapting to global warming. Yet its precise impact on local climate remains uncertain, a factor that complicates decision-making when it comes to prioritizing investments. Here, we developed global maps illustrating how natural forest regeneration influences key local climate drivers-land surface temperature (LST), albedo, and evapotranspiration-using models fitted at a 1-km spatial resolution with a random forest classifier. We found that natural forest regeneration can alter annual mean LST by 0.01 degrees C, -0.59 degrees C, -0.50 degrees C, and -2.03 degrees C in Boreal, Mediterranean, Temperate, and Tropical regions, respectively. These variations underscore the region-specific effects of forest regeneration. Importantly, natural forest regeneration reduces LST across 64% of 1 billion hectares and 75% of 148 million hectares of potentially restorable land under different scenarios. These findings improve understanding of how forest regeneration can help regulate local climate, supporting climate adaptation efforts. Natural forest regeneration can enhance local climate adaptation by reducing land surface temperature. Natural forest regenerations reduce mean land surface temperature across potentially restorable areas in the Boreal, Mediterranean, Temperate, and Tropical regions according to an analysis that combines climate data, machine learning, and scenario analysis.

About three quarter of Swiss residents live in urban areas, and this proportion is expected to grow in future decades. An increasing number of people will therefore be exposed to urban heat, which can have adverse effects on human wellbeing, productivity and physical health.We explore the possibility to detect high-risk areas in five Swiss cities with the development of an urban heat-based risk-mapping approach. The included cities are Basel, Bern, Geneva, Lausanne and Zurich. The analysis is based on a combination of biophysical, including Landsat 8 derived Land Surface Temperature (LST), and socioeconomic data. Additionally, we assess the impact of urban trees on urban heat within the districts of these cities, helping to estimate how risk levels would change under two scenarios: one with increased tree cover (MaxTree) and another with no (NoTree) urban trees.The assessment on the impact of urban trees on heat showed that the areas with urban trees generally experience cooler temperatures compared to those without, both at the city and district levels. This underscores the positive role of urban trees in mitigating the urban heat effect.The risk mapping approach revealed a distinct spatial pattern for each city and high risk areas were identified.Generally, the high-risk areas in the analyzed cities cover the city centers and areas with high vulnerability.The ‘NoTree’ scenario showed higher risks compared to the baseline situation, illustrating that urban trees currently mitigate heat related risks in Swiss cities. The ‘MaxTree’ scenario results in lower risks, especially in the cities of Lausanne and Bern.The presented risk mapping approach, including the two idealized scenarios, can be used by policy- and decision-makers (e.g. city planners) can be a tool to determine where urban planning actions are the most urgent and where trees could be most beneficial in terms of adaptation to heat. The approach is easily adaptable and transferable to other cities, since it relies on a clear and simple methodological framework, openly available LST data, and basic socioeconomic variables at district scale that are available for many cities.

Biodiversity loss, land degradation, and climate change are acknowledged environmental challenges faced by humanity. Human activities including land-use changes are key stressors for biodiversity, thus, future projections of biodiversity impacts need to include both climate change and land-use change. While a lot of studies focused on mapping and projecting the vulnerability of multiple species based on different climate mitigation scenarios or warming levels, land-use trajectories are often not included in these projections. Recent work made first steps to address these deficiencies. For example, Hof et al. (2018) evaluated potential future impacts of climate and land-use changes on global species richness of terrestrial vertebrates under a low and high emission scenario. However, they used the same land-use change assumptions for both emission scenarios. In this study, we aim to fill the described research gap by combining future climate scenarios and a matrix of land-use projections derived from integrated assessment modeling (IAM) to estimate the fractional land-use patterns, underlying land-use transitions, and key agricultural management information, to assess the impact of climate change on biodiversity and quantify the additional impact of land-use change. To this end, we use the global simulations with a species distribution model from the Hof et al. (2018) study forced by four GCMs and both RCP2.6 and RCP6.0 climate scenarios following the ISIMIP2b simulation protocol and apply a land-use filter on the species occurrence probabilities to determine the implications for the world’s amphibians, mammals and reptiles at a 0.5° resolution. The land use data used to include future projections of land-use change is the Land Use Harmonization dataset v2 (LUH2). LUH2 reconstructs and projects changes in land use among 12 categories. To match the species’ habitat preferences, data from IUCN Habitat and Classification Scheme for each species is mapped onto the 12 land-use types represented in the LUH2 dataset according to the conversion table from Carlson et al. (2022). The land-use data is then used to refine the climatic envelope and filter out regions where species cannot persist. This approach allows to quantify the change of the proportion of affected species distributions between different climate and land-use scenarios and combinations of both. In addition, it provides quantitative information on the impact of future climate change on biodiversity accounting for the combination of land-use change projections and climate-driven species distribution models. Key Reference: Hof, C., Voskamp, A., Biber, M. F., Böhning-Gaese, K., Engelhardt, E. K., Niamir, A., Willis, S. G., & Hickler, T. 2018: Bioenergy cropland expansion may offset positive effects of climate change mitigation for global vertebrate diversity. Proceedings of the National Academy of Sciences of the United States of America, 115(52), 13294–13299.

Water scarcity is a growing concern in many regions worldwide, as demand for clean water increases and supply becomes increasingly uncertain under climate change. Already today, more than 4 billion people experience water scarcity at least one month per year (Mekonnen and Hoekstra, 2016). Developing socio-economic conditions and growing population increase water demands, while climate change leads to changes in freshwater availability. Most studies quantify water scarcity in discrete time windows, with fixed population and climate change signals (e.g., 30 years or long-term averages). Recently, however, Thiery et al. (2021) proposed a novel approach, in which climate change impacts are integrated over a person's lifetime. In this cohort perspective, lifetime impact values are comparable across generations and regions. Evaluating this perspective for natural hazards, they showed, for example, that a newborn will experience a sixfold increase in drought exposure compared to a 60-year-old (Thiery et al., 2021). In this study, we use this cohort perspective to study how much water scarcity a person experiences during their lifetime. Based on monthly fluctuations in water demand and availability, we estimate the total amount of water demand not met and refer to it as 'lifetime water deficit'. To this end, we use an ensemble of four global hydrological models (MATSIRO, CWatM, LPJmL and H08), each forced by four GCMs and two RCP scenarios from the InterSectoral Impact Model Intercomparison Project (ISIMIP2b). The simulations account for varying population and socio-economic conditions in the historical and future period, following the SSP2 scenario. Combined with country-based population, cohort distribution and life expectancies, lifetime water deficits are quantified for different generations on a country level. Our findings reveal high water lifetime deficit values for regions that are already water scarce today, such as the Mediterranean, North Africa and the Middle East. In these regions, more than 70% of the lifetime water demand is not met when needed. Further comparison reveals differences in spatial, intergenerational and climate change scenarios, and provides information on different scenarios. Overall, this study provides a new perspective on quantifying water scarcity and the climate and population impacts. References:Mekonnen, M. M., & Hoekstra, A. Y. (2016). Four billion people facing severe water scarcity. Science Advances, 2(2). https://doi.org/10.1126/sciadv.1500323Thiery, W., Lange, S., Rogelj, J., Schleussner, C. F., Gudmundsson, L., Seneviratne, S. I., Andrijevic, M., Frieler, K., Emanuel, K., Geiger, T., Bresch, D. N., Zhao, F., Willner, S. N., Büchner, M., Volkholz, J., Bauer, N., Chang, J., Ciais, P., Dury, M., … Wada, Y. (2021). Intergenerational inequities in exposure to climate extremes. Science, 374(6564), 158–160. https://doi.org/10.1126/science.abi7339

Anthropogenic land use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently being extensively investigated within the World Climate Research Program (WCRP) Coordinated Downscaling Experiment (CORDEX) Flagship Pilot Study (FPS) Land Use and Climate Across Scales (LUCAS) using an ensemble of different regional climate models (RCMs) coupled with diverse land surface models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM-LSM simulations. To account for these needs, we generated the LUCAS Land Use and land Cover change (LUC) dataset version 1.1 at 0.1(degrees) resolution for Europe with annual LULC maps from 1950 to 2100 (https://doi.org/10.26050/WDCC/LUC_hist_EU_v1.1, Hoffmann et al., 2022b, https://doi.org/10.26050/WDCC/LUC_future_EU_v1.1, Hoffmann et al., 2022a), which is tailored to use in state-of-the-art RCMs. The plant functional type (PFT) distribution for the year 2015 (i.e. the Modelling human LAND surface Modifications and its feedbacks on local and regional cliMATE - LANDMATE - PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details on the conversion method, cross-walking procedure, and evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (2022b). Subsequently, we applied the land use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25(degrees) resolution as input for Coupled Modelling Intercomparison Project Phase 6 (CMIP6) experiments, to derive LULC distributions at high spatial resolution and at annual time steps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a land use translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different shared socioeconomic pathway (SSP) and representative concentration pathway (RCP) combinations used in the framework of CMIP6. The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions worldwide.

Land-use and land-cover changes (hereafter simply "land use") alter climates biogeophysically by affecting surface fluxes of energy and water. Yet, near-surface temperature responses to land use across observational versus model -based studies and spatial-temporal scales can be inconsistent. Here we assess the prevalence of the historical land use signal of daily maximum temperatures averaged over the warmest month of the year (tLU) using regularized optimal fingerprint-ing for detection and attribution. We use observations from the Climatic Research Unit and Berkeley Earth alongside his-torical simulations with and without land use from phase 6 of the Coupled Model Intercomparison Project to reconstruct an experiment representing the effects of land use on climate. To assess the signal of land use at spatially resolved conti-nental and global scales, we aggregate all input data across reference regions and continents, respectively. At both scales, land use does not comprise a significantly detectable set of forcings for two of four Earth system models and their multimo-del mean. Furthermore, using a principal component analysis, we find that tLU is mostly composed of the nonlocal effects of land use rather than its local effects. These findings show that, at scales relevant for climate attribution, uncertainties in Earth system model representations of land use are too high relative to the effects of internal variability to confidently assess land use.