High-resolution Climate Information Research Articles

CORDEX—Advancing High-Resolution Climate Information and Its Use in Society

CORDEX—Advancing High-Resolution Climate Information and Its Use in Society

PyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information

Abstract. The nature and severity of climate change impacts vary significantly from region to region. Consequently, high-resolution climate information is needed for meaningful impact assessments and the design of mitigation strategies. This demand has led to an increase in the application of empirical-statistical downscaling (ESD) models to general circulation model (GCM) simulations of future climate. In contrast to dynamical downscaling, the perfect prognosis ESD (PP-ESD) approach has several benefits, including low computation costs, the prevention of the propagation of GCM-specific errors, and high compatibility with different GCMs. Despite their advantages, the use of ESD models and the resulting data products is hampered by (1) the lack of accessible and user-friendly downscaling software packages that implement the entire downscaling cycle, (2) difficulties reproducing existing data products and assessing their credibility, and (3) difficulties reconciling different ESD-based predictions for the same region. We address these issues with a new open-source Python PP-ESD modeling framework called pyESD. pyESD implements the entire downscaling cycle, i.e., routines for data preparation, predictor selection and construction, model selection and training, evaluation, utility tools for relevant statistical tests, visualization, and more. The package includes a collection of well-established machine learning algorithms and allows the user to choose a variety of estimators, cross-validation schemes, objective function measures, and hyperparameter optimization in relatively few lines of code. The package is well-documented, highly modular, and flexible. It allows quick and reproducible downscaling of any climate information, such as precipitation, temperature, wind speed, or even short-term glacier length and mass changes. We demonstrate the use and effectiveness of the new PP-ESD framework by generating weather-station-based downscaling products for precipitation and temperature in complex mountainous terrain in southwestern Germany. The application example covers all important steps of the downscaling cycle and different levels of experimental complexity. All scripts and datasets used in the case study are publicly available to (1) ensure the reproducibility and replicability of the modeled results and (2) simplify learning to use the software package.

Climatic comparison between Belgium, Champagne, Alsace, Jura and Bourgogne for wine production using the regional model MAR

In Belgium, vineyards have strongly increased over the last decades. Is it a trendy effect, or is Belgium becoming an increasingly favourable country for viticulture? A related issue is whether Belgium is similar to another French region from a climatic point of view. To address these questions, we use here the regional climate model MAR to provide high-resolution (5 km) climate information over the territory of Belgium and the north-eastern quarter of France. We first evaluate MAR outputs from a climate point of view against more than 150 weather stations and then from a viticulture point of view by computing bioclimatic indices, as well as key phenological dates and frost risk. The second step consists in comparing the four northernmost French wine regions (Champagne, Bourgogne, Jura and Alsace) with the Belgian wine region. MAR simulations are generally consistent with the observation, especially for the dates of the main phenological stages of the vine. Simulations of a frost risk in spring, heat stress in summer and Huglin’s heliothermal index show slightly more disagreement, but biases remain moderate. The Belgium wine region appears to be quite comparable to the Champagne and Jura regions, despite colder conditions that influence its bioclimatic indices. Under current climate conditions, the main risk for Belgian vines is frost after bud break.

Evaluation of ALARO-0 and REMO Regional Climate Models over Iran Focusing on Building Material Degradation Criteria

Understanding how climate change affects material degradation is the first step in heritage conservation. To study such impact, high-resolution climate information is required. However, so far, no regional climate simulations have been evaluated considering building damage criteria over the region of Iran. This paper has a twofold objective: to conduct an overview of climate model performance over Iran by evaluating the output of two regional climate models, ALARO-0 and REMO2015, and to find an optimal approach for model evaluation fitted to studies on building physics. Data of the evaluation run for both models were compared with data of weather stations located in six different climate zones in Iran to assess their performance over the region and gain insight about model uncertainties. Given that the research scope covers the evaluation of climate models to use in studies on building physics, in addition to climate parameters, five degradation risks are analysed. The performance of the two models varies over the studied locations. In general, both models fall within the spread of observations except for wind parameters. Accordingly, indices related to temperature and precipitation are well predicted, in contrast to indices related to wind. The analysis shows that considering the observed biases, selecting an ensemble of representative models based on the evaluation results of climate variables important for hygrothermal simulations would be recommended.

Crop-specific exposure to extreme temperature and moisture for the globe for the last half century

Global assessments of climate extremes typically do not account for the unique characteristics of individual crops. A consistent definition of the exposure of specific crops to extreme weather would enable agriculturally-relevant hazard quantification. To this end, we develop a database of both the temperature and moisture extremes facing individual crops by explicitly accounting for crop characteristics. To do this, we collate crop-specific temperature and moisture parameters from the agronomy literature, which are then combined with time-varying crop locations and high-resolution climate information to quantify crop-specific exposure to extreme weather. Specifically, we estimate crop-specific temperature and moisture shocks during the growing season for a 0.25∘ spatial grid and daily time scale from 1961 to 2014 globally. We call this the Agriculturally-Relevant Exposure to Shocks (ARES) model and make all ARES output available with this paper. Our crop-specific approach leads to a smaller average value of the exposure rate and spatial extent than does a crop-agnostic approach. Of the 17 crops included in this study, 13 had an increase in exposure to extreme heat, while 9 were more exposed to extreme cold over the past half century. All crops in this study show a statistically significant increase in exposure to both extreme wetness and dryness. Cassava, sunflowers, soybeans, and oats had the greatest increase in hot, cold, dry, and wet exposure, respectively. We compare ARES model results with the EM-DAT disaster database. Our results highlight the importance of crop-specific characteristics in defining weather shocks in agriculture.

Multi-decadal convection-permitting climate projections for China’s Greater Bay Area and surroundings

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is the world’s largest bay area in terms of land area and population, which has been increasingly suffering from weather and climate extremes under global warming. It is thus desired to produce reliable high-resolution climate information at a regional scale in order to enhance resilience to climate change over the GBA. For the first time, this study develops the multi-decadal nested-grid climate projections at a convection-permitting scale for the GBA, and assesses the abilities of the Weather Research and Forecasting (WRF) model with 36-, 12- and 4 km resolutions in representing precipitation, temperature and their extremes. Our findings indicate the added value of the convection-permitting WRF model for simulating the spring and summertime precipitation as well as extreme heavy rainfall events with daily amounts larger than 30 mm over the GBA. Increasing the spatial resolution of the WRF model does not necessarily lead to a significant improvement on temperature simulations. In addition, our findings reveal that the GBA is expected to have an increasing number of heavy and extreme heavy rainfall events by the end of the twenty-first century. Moreover, the GBA is projected to have a large temperature change across different seasons, and an enhanced warming will appear in autumn. The GBA is also expected to have more summer days with longer durations, thereby leading to an increasing risk of heatwaves and heat stress.

Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain

Abstract. To allow for climate impact studies on human and natural systems, high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. The CORDEX Central Asia domain is one of these regions, and this article describes the evaluation for two regional climate models (RCMs), REMO and ALARO-0, that were run for the first time at a horizontal resolution of 0.22∘ (25 km) over this region. The output of the ERA-Interim-driven RCMs is compared with different observational datasets over the 1980–2017 period. REMO scores better for temperature, whereas the ALARO-0 model prevails for precipitation. Studying specific subregions provides deeper insight into the strengths and weaknesses of both RCMs over the CAS-CORDEX domain. For example, ALARO-0 has difficulties in simulating the temperature over the northern part of the domain, particularly when snow cover is present, while REMO poorly simulates the annual cycle of precipitation over the Tibetan Plateau. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. This study aims to evaluate whether REMO and ALARO-0 provide reliable climate information over the CAS-CORDEX domain for impact modeling and environmental assessment applications. Depending on the evaluated season and variable, it is demonstrated that the produced climate data can be used in several subregions, e.g., temperature and precipitation over western Central Asia in autumn. At the same time, a bias adjustment is required for regions where significant biases have been identified.

Climate change and rural–urban migration in the Brazilian Northeast region

Climate change and its potentially harmful effects on agricultural production, income, and subsistence might change the incentive and capability of the population to remain in rural areas or to migrate to urban locations. Using census micro-data in combination with high-resolution climate information, we explore the impacts of climate change on rural–urban migration in the Brazilian Northeast region. Results from a gravity model estimation reveal that the climate–migration relationship depends on the agricultural income levels of rural origin areas and the educational attainment of the rural population. Specifically, our results indicate that the intensification of climate adversities may have contributed to boosting migration from rural areas with lower socioeconomic vulnerability. In contrast, in the most deprived rural areas, harmful climate effects may have resulted in the reduction of this type of migration flow. Nevertheless, our findings suggest that education might attenuate the suppressing effects of adverse climate conditions on migration in highly vulnerable rural areas, suggesting a viable pathway to overcome mobility constraints. Our findings emphasize the complexity of climate–migration linkages and conclude that the debate on climate change and migration should no longer consider that climate change invariably results in migration, but also should investigate who is able to implement and take advantage of migration as an adaptation strategy. Policies to address issues related to climate-induced migration must focus on both facilitating migration and assisting vulnerable segments of the population who remain in place, as the less-educated rural population whose livelihoods depend on the agricultural activity.

Towards an End-to-End Analysis and Prediction System for Weather, Climate, and Marine Applications in the Red Sea

AbstractThe Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more.

Planning for Climate Change

Problem, research strategy, and findings Climate scientists have developed high-resolution climate models to project local effects of climate change. However, such high-resolution climate information is not widely incorporated into climate change adaptation plans. In this study we analyze high-resolution (1.24 × 1.24-mile grid) temperature data generated by a climate model for Los Angeles County (CA). The data include projected surface air temperature and the number of extreme heat days (daily maximum temperature exceeding 95 °F) between a baseline period (1981–2000) and a mid-21st-century period (2041–2060). The data assume that human-induced greenhouse gas emissions will continuously increase at the current level and no change in existing built environments. We find significant geographic heterogeneity in terms of climate change effects in Los Angeles County. Also, given the assumptions, we expect inland suburban areas where urban expansion has been occurring to have more severe extreme heat effects than central and coastal areas by mid-century. Takeaway for practice Planners should engage with climate scientists to access and use high-resolution climate data. The use of high-resolution climate data needs to be mandated in plan-making in the era of climate change. Many regions may have different spatial patterns of extreme heat events and require tailored climate adaptation plans. In Los Angeles County, for example, continuing suburbanization in inland valley areas may need to be discouraged, whereas coastal areas with the least effects may consider denser, inclusive redevelopment strategies.

The Ongoing Need for High-Resolution Regional Climate Models: Process Understanding and Stakeholder Information

ABSTRACTRegional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that fine-scale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.

Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment

Abstract. The increasing demand for high-resolution climate information has attracted growing attention to statistical downscaling (SDS) methods, due in part to their relative advantages and merits as compared to dynamical approaches (based on regional climate model simulations), such as their much lower computational cost and their fitness for purpose for many local-scale applications. As a result, a plethora of SDS methods is nowadays available to climate scientists, which has motivated recent efforts for their comprehensive evaluation, like the VALUE initiative (http://www.value-cost.eu, last access: 29 March 2020). The systematic intercomparison of a large number of SDS techniques undertaken in VALUE, many of them independently developed by different authors and modeling centers in a variety of languages/environments, has shown a compelling need for new tools allowing for their application within an integrated framework. In this regard, downscaleR is an R package for statistical downscaling of climate information which covers the most popular approaches (model output statistics – including the so-called “bias correction” methods – and perfect prognosis) and state-of-the-art techniques. It has been conceived to work primarily with daily data and can be used in the framework of both seasonal forecasting and climate change studies. Its full integration within the climate4R framework (Iturbide et al., 2019) makes possible the development of end-to-end downscaling applications, from data retrieval to model building, validation, and prediction, bringing to climate scientists and practitioners a unique comprehensive framework for SDS model development. In this article the main features of downscaleR are showcased through the replication of some of the results obtained in VALUE, placing an emphasis on the most technically complex stages of perfect-prognosis model calibration (predictor screening, cross-validation, and model selection) that are accomplished through simple commands allowing for extremely flexible model tuning, tailored to the needs of users requiring an easy interface for different levels of experimental complexity. As part of the open-source climate4R framework, downscaleR is freely available and the necessary data and R scripts to fully replicate the experiments included in this paper are also provided as a companion notebook.

Future changes in land and atmospheric variables: An analysis of their couplings in the Iberian Peninsula

This work investigates climate-change projections over a transitional region between dry and wet climates, the Iberian Peninsula (IP). With this purpose, the Weather Research and Forecasting (WRF) model, driven by two global climate models (CCSM4 and MPI-ESM-LR) previously bias-corrected, was used to generate high-resolution climate information. Simulations were carried out for two periods, 1980–2014 and 2071–2100, and under two representative concentration pathways (RCP4.5 and RCP8.5). The analysis focused on changes in land-surface processes, their causes, and the potential impact on the climate system. To achieve this, seasonal projected changes of land-surface (soil moisture and surface evapotranspiration) and atmospheric variables involved in the hydrologic (i.e., precipitation and runoff) and energy balance (i.e., temperature and solar incoming radiation) were investigated. The results reveal that the IP is likely to experience a soil dryness by the end of the 21st century, particularly during summer and fall, more apparent in the southern IP, and stronger under the RCP8.5. However, such trends would have different implications throughout the year and directly affect the surface evapotranspiration. Moreover, soil-drying trends are mainly associated with reductions in the large-scale precipitation during spring, summer, and fall and by enhanced evapotranspiration particularly in spring over the northwestern IP. In addition, the results show notably changes in soil conditions at high altitude, particularly during winter, which may alter the land-atmosphere processes that currently occur in these regions. In this context, noteworthy changes in the climate system are expected, leading to adverse impacts on water resources and temperature. The results highlight the complex and nonlinear nature of land-atmosphere interactions in regions such as the IP, which is a tremendous challenge for adequately developing mitigation and adaptation strategies to anthropogenic climate change.

The Australian National Rabbit Database: 50yr of population monitoring of an invasive species.

With ongoing introductions into Australia since the 1700s, the European rabbit (Oryctolagus cuniculus) has become one of the most widely distributed and abundant vertebrate pests, adversely impacting Australia's biodiversity and agroeconomy. To understand the population and range dynamics of the species and its impacts better, occurrence and abundance data have been collected by researchers and citizens from sites covering a broad spectrum of climatic and environmental conditions in Australia. The lack of a common and accessible repository for these data has, however, limited their use in determining important spatiotemporal drivers of the structure and dynamics of the geographical range of rabbits in Australia. To meet this need, we created the Australian National Rabbit Database, which combines more than 50yr of historical and contemporary survey data collected from throughout the range of the species in Australia. The survey data, obtained from a suite of complementary monitoring methods, were combined with high-resolution weather, climate, and environmental information, and an assessment of data quality. The database provides records of rabbit occurrence (689,265 records) and abundance (51,241 records, >120 distinct sites) suitable for identifying the spatiotemporal drivers of the rabbit's distribution and for determining spatial patterns of variation in its key life-history traits, including maximum rates of population growth. Because all data are georeferenced and date stamped, they can be coupled with information from other databases and spatial layers to explore the potential effects of rabbit occurrence and abundance on Australia's native wildlife and agricultural production. The Australian National Rabbit Database is an important tool for understanding and managing the European rabbit in its invasive range and its effects on native biodiversity and agricultural production. It also provides a valuable resource for addressing questions related to the biology, success, and impacts of invasive species more generally. No copyright or proprietary restrictions are associated with the use of this data set other than citation of this Data Paper.

Comparing the Performance of Dynamical and Statistical Downscaling on Historical Run Precipitation Data over a Semi-Arid Region

Precise evaluations of climate model precipitation outputs are valuable for making decisions regarding agriculture, water resource, and ecosystem management. Many downscaling techniques have been developed in the past few years for projection of weather variables. We need to apply dynamical and statistical downscaling (DD and SD) to bridge the gap between the coarse resolution general circulation model (GCM) outputs and the need for high-resolution climate information over a semi-arid region. We compare the requirements of DD (RegCM4) and SD (Delta) approaches, evaluate the historical run of NNRP1 data in comparison with station data, and analyze the changes in wet days and precipitation values through both methods during 1990–2010. In this study, we did not want to use prediction data under different scenarios of climate change, and we have just applied observed data to assess the amount of precise of NNRP1 data, over the observed period. SD method requires less time and computing power than DD. The DD approach performs better over the evaluation period according to efficiency criteria. In general, the Pearson correlation in DD with observation data in evaluation period was higher than (r > 0.72 and R2 > 0.52) SD (r > 0.65 and R2 > 0.41) over three study stations. Similarly, MAE and NSE show better results from DD relative to SD. SD underestimates the number annual mean wet-days for all three stations examined. DD overestimates a number of annual mean wet-days, but with less deviation from the observed mean.

Exploring the link between climate variability and mortality in Sub-Saharan Africa

High mortality rates in Sub-Saharan Africa (SSA) persist, delaying achievement of the sustainable development goals (SDGs)1. We investigated whether climate variability contributed to elevated mortality in rural Kenya, Mali, and Malawi during 2008/20092. We linked high-resolution climate information to nationally representative census data from the Terra Populus data extraction system using multilevel negative binomial models to estimate the association between household-level mortality and climate variability from a long-term climate normal period (1961-1990). Results revealed cold snaps increased mortality in Kenya but reduced mortality in Mali and Malawi. Excessive precipitation and droughts were associated with increased mortality in Kenya and Malawi. Adverse climatic conditions increased mortality in regions with high HIV/AIDS prevalence, but reduced mortality in areas with high malaria prevalence. Programs for reducing climate-related mortality through early warning systems, agricultural extension services, and improved access to health infrastructure will help more fully realise the SDGs of mortality reduction for SSA.

Morphological diversity of Quercus fossil pollen in the northern South China Sea during the last glacial maximum and its paleoclimatic implication.

We aimed to obtain high resolution vegetation data and climate information about the LGM in the inland of the northern SCS based on key pollen types. Dominant Quercus fossil pollen grains in the core from the continental shelf of the northern SCS have been identified at the infrageneric level by using scanning electron microscopy. Based on tectum ornamentation, we recognized five sculpture types of Quercus pollen, namely, rodlike, rodlike masked, rodlike vertical, verrucate and micro-verrucate. Such a high diversity of Quercus fossil pollen types indicated that broadleaved forests were widely distributed in the inland along the northern SCS and included species of the subgenera Cyclobalanopsis and Quercus, of which subgenus Cyclobalanopsis populations were highly dominant. Low abundance of deciduous Quercus pollen probably derived from temperate-subtropical forests, while abundant evergreen pollen types of subgenura Quercus and Cyclobalanopsis, as well as other pollen of broadleaved taxa in the pollen assemblages, strongly suggest that the inland has been covered by dense subtropical forests. Consequently, the warm and humid subtropical climate prevailed during the LGM in the inland along the northern SCS. Our results shed new light on regional climatic conditions during the LGM in eastern Asia based on high diversity of Quercus fossil pollen in marine deposits from northern SCS.

High-resolution climate projections for South Asia to inform climate impacts and adaptation studies in the Ganges-Brahmaputra-Meghna and Mahanadi deltas

Climate impacts and adaptation studies often use output from impact models that require data representing future climates at a resolution greater than can be provided by Global Climate Models (GCMs). This paper describes the use of Regional Climate Model (RCM) simulations to generate high-resolution future climate information for assessing climate impacts in the Ganges-Brahmaputra-Meghna (GBM) and Mahanadi deltas as part of the DECCMA project. In this study, three different GCMs (HadGEM2-ES, CNRM-CM5 and GFDL-CM3), all using a single scenario for future greenhouse forcing of the atmosphere (RCP 8.5), were downscaled to a horizontal resolution of 25 km over south Asia using the HadRM3P RCM. These three GCMs were selected based on ability to represent key climate processes over south Asia and ability to sample a range of regional climate change responses to greenhouse gas forcing. RCM simulations of temperature, precipitation, and lower level (850 hPa) atmospheric circulation in the monsoon season (June, July, August, September – JJAS) were compared with observational datasets and their respective driving GCMs to ensure large-scale consistency. Although there are some biases in the RCM simulations, these comparisons indicate that the RCMs are able to simulate realistically aspects of the observed climate of South Asia, such as the monsoon circulation and associated precipitation that are key for informing downstream impacts and adaptation studies. Simulated future temperature and precipitation changes on seasonal and daily timescales suggest increases in both temperature and precipitation across all three models during the monsoon season, with an increase in the amount of extremely heavy precipitation over the GBM and Mahanadi basins. Despite different driving conditions, these results are consistent across all three RCM simulations, providing a level of confidence in the magnitudes and spatial characteristics of temperature and precipitation projections for South Asia.

The CORDEX.be initiative as a foundation for climate services in Belgium

The CORDEX.be project created the foundations for Belgian climate services by producing high-resolution Belgian climate information that (a) incorporates the expertise of the different Belgian climate modeling groups and that (b) is consistent with the outcomes of the international CORDEX (“COordinated Regional Climate Downscaling Experiment”) project. The key practical tasks for the project were the coordination of activities among different Belgian climate groups, fostering the links to specific international initiatives and the creation of a stakeholder dialogue. Scientifically, the CORDEX.be project contributed to the EURO-CORDEX project, created a small ensemble of High-Resolution (H-Res) future projections over Belgium at convection-permitting resolutions and coupled these to seven Local Impact Models. Several impact studies have been carried out. The project also addressed some aspects of climate change uncertainties. The interactions and feedback from the stakeholder dialogue led to different practical applications at the Belgian national level.

Assessment of the Performance of Three Dynamical Climate Downscaling Methods Using Different Land Surface Information over China

This study aims to assess the performance of different dynamical downscaling methods using updated land surface information. Particular attention is given to obtaining high-resolution climate information over China by the combination of an appropriate dynamical downscaling method and updated land surface information. Two group experiments using two land surface datasets are performed, including default Weather Research and Forecasting (WRF) land surface data (OLD) and accurate dynamically accordant MODIS data (NEW). Each group consists of three types of experiments for the summer of 2014, including traditional continuous integration (CT), spectral nudging (SN), and re-initialization (Re) experiments. The Weather Research and Forecasting (WRF) model is used to dynamically downscale ERA-Interim (reanalysis of the European Centre for Medium-Range Weather Forecast, ECMWF) data with a grid spacing of 30 km over China. The simulations are evaluated via comparison with observed conventional meteorological variables, showing that the CT method, which notably overestimates 2 m temperature and underestimates 2 m relative humidity across China, performs the worst; the SN and Re runs outperform the CT method, and the Re shows the smallest RMSE (root means square error). A comparison of observed and simulated precipitation shows that the SN simulation is closest to the observed data, while the CT and Re simulations overestimate precipitation south of the Yangtze River. Compared with the OLD group, the RMSE values of temperature and relative humidity are significantly improved in CT and SN, and there is smaller improved in Re. However, obvious improvements in precipitation are not evident.