Gridded Meteorological Dataset Research Articles

Non-linear relationships between daily temperature extremes and US agricultural yields uncovered by global gridded meteorological datasets

Global agricultural commodity markets are highly integrated among major producers. Prices are driven by aggregate supply rather than what happens in individual countries in isolation. Estimating the effects of weather-induced shocks on production, trade patterns and prices hence requires a globally representative weather data set. Recently, two data sets that provide daily or hourly records, GMFD and ERA5-Land, became available. Starting with the US, a data rich region, we formally test whether these global data sets are as good as more fine-scaled country-specific data in explaining yields and whether they estimate similar response functions. While GMFD and ERA5-Land have lower predictive skill for US corn and soybeans yields than the fine-scaled PRISM data, they still correctly uncover the underlying non-linear temperature relationship. All specifications using daily temperature extremes under any of the weather data sets outperform models that use a quadratic in average temperature. Correctly capturing the effect of daily extremes has a larger effect than the choice of weather data. In a second step, focusing on Sub Saharan Africa, a data sparse region, we confirm that GMFD and ERA5-Land have superior predictive power to CRU, a global weather data set previously employed for modeling climate effects in the region.

Evaluation of CRU-JRA gridded meteorological dataset for modeling of wheat production systems in Iran.

Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates.

IMDLIB: An open-source library for retrieval, processing and spatiotemporal exploratory assessments of gridded meteorological observation datasets over India

Addressing a pressing need for streamlined access to observed meteorological data in India, we present IMDLIB, an open-source Python library that operates on gridded datasets from the India Meteorological Department (IMD). IMDLIB simplifies the retrieval, management, and processing of IMD's rainfall, maximum and minimum temperature data at varying scales (point, regional, catchment, and country scales). IMDLIB stands out by directly interfacing with IMD's database through robust APIs, streamlining data acquisition. It offers transformation of IMD's binary data into common formats (e.g., NetCDF/GeoTIFF/CSV) and advanced analytics, including trend analysis, spatiotemporal data interpolation, calculation of meteorological indices and extreme characteristics, showcasing superior capabilities compared to traditional meteorological data processing tool available for the region. To underline its robustness, IMDLIB's application was illustrated in two distinct regions: Rajasthan State and the Godavari River Basin. Through IMDLIB, we envision enhanced data accessibility, leading to more insightful hydro-climatic research and analysis.

Analyzing temperature and precipitation extremes in China using multiple gridded datasets: A comparative evaluation

Gridded meteorological datasets offer valuable data sources for monitoring and studying climate extremes. Based on the 2291 CMA meteorological observations, this paper evaluates and compares seven high-resolution gridded datasets and a global climate extremes indices datasets, HadEX3. Temporal and spatial consistency are assessed at the location of CMA observations. High-resolution gridded products and HadEX3 can effectively monitor the inter-annual changes in the overall mean of extreme temperature. The temporal consistency of precipitation indices is lower due to uneven spatial distribution of precipitation. Each high-resolution gridded dataset shows good consistency in depicting the spatial distribution patterns of extreme climate. However, at locations of the observations, different indices have larger or smaller biases. These are mainly due to the spatial scale of gridded datasets and calculation and operation order of extreme indices. The study emphasizes the importance of in-situ observations in improving data accuracy. At the same time, it is found that the generation method of the dataset has an impact on the quality, and satellite and model simulation data as background fields can compensate for the lack of spatial coverage of int-situ observations. In the study of extreme climate, appropriate datasets should be selected according to different research purposes.

Hydrological Modeling in the Upper Lancang-Mekong River Basin Using Global and Regional Gridded Meteorological Re-Analyses

Multisource meteorological re-analyses provide the most reliable forcing data for driving hydrological models to simulate streamflow. We aimed to assess different hydrological responses through hydrological modeling in the upper Lancang-Mekong River Basin (LMRB) using two gridded meteorological datasets, Climate Forecast System Re-analysis (CFSR) and the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS). We selected the Pearson’s correlation coefficient (R), percent bias (PBIAS), and root mean square error (RMSE) indices to compare the six meteorological variables of the two datasets. The spatial distributions of the statistical indicators in CFSR and CMADS, namely, the R, PBIAS, and RMSE values, were different. Furthermore, the soil and water assessment tool plus (SWAT+) model was used to perform hydrological modeling based on CFSR and CMADS meteorological re-analyses in the upper LMRB. The different meteorological datasets resulted in significant differences in hydrological responses, reflected by variations in the sensitive parameters and their optimal values. The differences in the calibrated optimal values for the sensitive parameters led to differences in the simulated water balance components between the CFSR- and CMADS-based SWAT+ models. These findings could help improve the understanding of the strengths and weaknesses of different meteorological re-analysis datasets and their roles in hydrological modeling.

Ensemble Dressing of Meteorological Fields: Using Spatial Regression to Estimate Uncertainty in Deterministic Gridded Meteorological Datasets

Abstract Most datasets of surface meteorology are deterministic, yet many applications using these datasets require or can benefit from uncertainty estimates in meteorological fields. Motivated by this gap, we evaluated the use of a spatial regression method to estimate the uncertainty in precipitation and temperature fields of existing deterministic gridded meteorological datasets. Taking the widely used North American Land Data Assimilation System 2 (NLDAS-2) precipitation and temperature dataset as an example, we used the deterministic NLDAS-2 values to generate ensemble estimates for daily precipitation, mean temperature, and the diurnal temperature range. Our method is a form of ensemble dressing. Nine variations were tested to assess the impacts of sampling density on the estimates of the mean and uncertainty, and one strategy was selected to generate 100 ensemble members at 1/8° and daily resolution for the period 1979–2019, termed as the Ensemble Dressing of NLDAS-2 (EDN2). Compared with an independent station-based ensemble dataset, the ensemble dressing method produces reasonable uncertainty patterns for precipitation and underestimates uncertainty for temperature. For precipitation, the uncertainty increases with the increase in daily accumulation. For temperature, the uncertainty is relatively small in the warm season and large in the cold season. This ensemble dressing method is applicable to other deterministic gridded meteorological datasets. The generated spatiotemporally varying uncertainty information could support applications such as land surface and hydrologic modeling, data assimilation, and forecasting, especially where application models are tied to a specific meteorological dataset.

Generating daily high-resolution gridded meteorological datasets for New York State from 2017-2018 using two-stage downscaling model

Generating daily high-resolution gridded meteorological datasets for New York State from 2017-2018 using two-stage downscaling model

EMO-5: a high-resolution multi-variable gridded meteorological dataset for Europe

Abstract. In this paper we present EMO-5 (“European Meteorological Observations”, spatial resolution of 5 km), a European high-resolution, (sub-)daily, multi-variable meteorological dataset built on historical and real-time observations obtained by integrating data from 18 964 ground weather stations, four high-resolution regional observational grids (i.e. CombiPrecip, ZAMG – INCA, EURO4M-APGD, and CarpatClim), and one global reanalysis (ERA-Interim/Land). EMO-5 includes the following at daily resolution: total precipitation, temperatures (minimum and maximum), wind speed, solar radiation, and water vapour pressure. In addition, EMO-5 also makes available 6-hourly precipitation and mean temperature data. The raw observations from the ground weather stations underwent a set of quality controls before SPHEREMAP and Yamamoto interpolation methods were applied in order to estimate for each 5×5 km grid cell the variable value and its affiliated uncertainty, respectively. The quality of the EMO-5 precipitation data was evaluated through (1) comparison with two regional high-resolution datasets (i.e. seNorge2 and seNorge2018), (2) analysis of 15 heavy precipitation events, and (3) examination of the interpolation uncertainty. Results show that EMO-5 successfully captured 80 % of the heavy precipitation events, and that it is of comparable quality to a regional high-resolution dataset. The availability of the uncertainty fields increases the transparency of the dataset and hence the possible usage. EMO-5 (version 1) covers the time period from 1990 to 2019, with a near real-time release of the latest gridded observations foreseen with version 2. As a product of Copernicus, the EU's Earth Observation Programme, the EMO-5 dataset is free and open, and can be accessed at https://doi.org/10.2905/0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26 (Thiemig et al., 2020).

Wet-Bulb Globe Temperature, Universal Thermal Climate Index, and Other Heat Metrics for US Counties, 2000–2020

Epidemiologic research on extreme heat consistently finds significant impacts on human morbidity and mortality. However, most of these analyses do not use spatially explicit measures of heat (typically assessing exposures at major cities using the nearest weather station), and they frequently consider only ambient temperature or heat index. The field is moving toward more expansive analyses that use spatially resolved gridded meteorological datasets and alternative assessments of heat, such as wet-bulb globe temperature (WBGT) and universal thermal climate index (UTCI), both of which require technical geoscientific skills that may be inaccessible to many public health researchers. To facilitate research in this domain, we created a database of population-weighted, spatially explicit daily heat metrics – including WBGT, UTCI, heat index, dewpoint temperature, net effective temperature, and humidex – for counties in the conterminous United States derived from the ERA5-Land gridded data set and using previously validated equations and algorithms. We also provide an R package to calculate these metrics, including gold-standard algorithms for estimating WBGT and UTCI, to facilitate replication.

Evaluation of daily gridded meteorological datasets for hydrological modeling in data-sparse basins of the largest lake in Southeast Asia

Study regionTonle Sap Lake (TSL) Basin in Cambodia. Study focusThe development and application of hydrological models for data-sparse basins are hindered by the limited hydro-meteorological data. Although gridded meteorological products are alternatively considered in many studies, the validation of the products with limited point observations overlooks the original spatiotemporal characteristics, thus leading to a selection of datasets with high uncertainty. Here, we evaluated seven gridded meteorological datasets of rainfall and air temperature covering the data-sparse Tonle Sap Lake Basin by employing the statistical approach based on the bilinear-interpolation method and hydrological approach using the SWAT model, which ensures the reliable estimates of streamflow and evapotranspiration. New hydrological insights for the regionThe results of the statistical approach indicate that APHRODITE, ERA5, TRMM and IMERG-based precipitation and CPC and SA-OBS-based air temperature performed comparably well (R ≥ 0.75) with the gauged data. However, ERA5-based streamflow performed relatively poor, while SWAT driven by APHRODITE underestimated satellite-based evapotranspiration, indicating the underestimation of basin-wide precipitation by APHRODITE. Although TRMM and IMERG provide more reliable estimation of streamflow and evapotranspiration, slightly better performance and a higher spatial resolution of IMERG dataset suggest that IMERG precipitation is superior for basin-wide hydrological modeling and impact assessment. These findings showed that statistical comparisons with gauge-data and hydrological evaluation of streamflow are not enough to justify the reliability of each gridded dataset.

Hydrological Modeling in the Chaohu Lake Basin of China—Driven by Open-Access Gridded Meteorological and Remote Sensing Precipitation Products

This study assessed the performance of two well-known gridded meteorological datasets, CFSR (Climate Forecast System Reanalysis) and CMADS (China Meteorological Assimilation Driving Datasets), and three satellite-based precipitation datasets, TRMM (Tropical Rainfall Measuring Mission), CMORPH (Climate Prediction Center morphing technique), and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), in driving the SWAT (Soil and Water Assessment Tool) model for streamflow simulation in the Fengle watershed in the middle–lower Yangtze Plain, China. Eighteen model scenarios were generated by forcing the SWAT model with different combinations of three meteorological datasets and six precipitation datasets. Our results showed that (1) the three satellite-based precipitation datasets (i.e., TRMM, CMORPH, and CHIRPS) generally provided more accurate precipitation estimates than CFSR and CMADS. CFSR and CMADS agreed fairly well with the gauged measurements in maximum temperature, minimum temperature, and relative humidity, but large discrepancies existed for the solar radiation and wind speed. (2) The impact of precipitation data on simulated streamflow was much larger than that of other meteorological variables. Satisfactory simulations were achieved using the CMORPH precipitation data for daily streamflow simulation and the TRMM and CHIRPS precipitation data for monthly streamflow simulation. This suggests that different precipitation datasets can be used for optimal simulations at different temporal scales.

SC-Earth: A Station-Based Serially Complete Earth Dataset from 1950 to 2019

AbstractMeteorological data from ground stations suffer from temporal discontinuities caused by missing values and short measurement periods. Gap-filling and reconstruction techniques have proven to be effective in producing serially complete station datasets (SCDs) that are used for a myriad of meteorological applications (e.g., developing gridded meteorological datasets and validating models). To our knowledge, all SCDs are developed at regional scales. In this study, we developed the serially complete Earth (SC-Earth) dataset, which provides daily precipitation, mean temperature, temperature range, dewpoint temperature, and wind speed data from 1950 to 2019. SC-Earth utilizes raw station data from the Global Historical Climatology Network–Daily (GHCN-D) and the Global Surface Summary of the Day (GSOD). A unified station repository is generated based on GHCN-D and GSOD after station merging and strict quality control. ERA5 is optimally matched with station data considering the time shift issue and then used to assist the global gap filling. SC-Earth is generated by merging estimates from 15 strategies based on quantile mapping, spatial interpolation, machine learning, and multistrategy merging. The final estimates are bias corrected using a combination of quantile mapping and quantile delta mapping. Comprehensive validation demonstrates that SC-Earth has high accuracy around the globe, with degraded quality in the tropics and oceanic islands due to sparse station networks, strong spatial precipitation gradients, and degraded ERA5 estimates. Meanwhile, SC-Earth inherits potential limitations such as inhomogeneity and precipitation undercatch from raw station data, which may affect its application in some cases. Overall, the high-quality and high-density SC-Earth dataset will benefit research in fields of hydrology, ecology, meteorology, and climate. The dataset is available athttps://zenodo.org/record/4762586.

An extreme-preserving long-term gridded daily precipitation data set for the conterminous United States

AbstractExtreme daily precipitation contributes to flooding that can cause significant economic damages, and so is important to properly capture in gridded meteorological data sets. This work examines precipitation extremes, the mean precipitation on wet days, and fraction of wet days in two widely used gridded data sets over the conterminous U.S. (CONUS). Compared to the underlying station observations, the gridded data show a 27% reduction in annual 1-day maximum precipitation, 25% increase in wet day fraction, 1.5 to 2.5 day increase in mean wet spell length, 30% low bias in 20-year return values of daily precipitation, and 25% decrease in mean precipitation on wet days. It is shown these changes arise primarily from the time-adjustment applied to put the precipitation gauge observations into a uniform time frame, with the gridding process playing a lesser role. A new daily precipitation data set is developed that omits the time-adjustment (as well as extending the gridded data by 7 years) and is shown to perform significantly better in reproducing extreme precipitation metrics. When the new data set is used to force a land surface model, annually averaged 1-day maximum runoff increases 38% compared to the original data, annual mean runoff increases 17%, evapotranspiration drops 2.3%, and fewer wet days leads to a 3.3% increase in estimated solar insolation. These changes are large enough to affect portrayals of flood risk and water balance components important for ecological and climate-change applications across the CONUS.

Evaluation of gridded meteorological datasets and their potential hydrological application to a humid area with scarce data for Pirapama River basin, northeastern Brazil

This work evaluated the simulation of streamflow using observed and estimated gridded meteorological datasets and the Soil and Water Assessment Tool (SWAT) model for a humid area with scarce data in northeastern Brazil. The coefficient of determination (R2), Nash-Sutcliffe efficiency (NS), root mean square error (RMSE), normalized root mean square error (NRMSE), and percent bias (PBIAS) were used to assess the SWAT results yielded by estimated and observed rainfall data. The hydrological modeling data from three streamflow stations were used (2000 to 2006 for calibration and 2007 to 2010 for validation). The results show that at daily scale, the estimated rainfall data show a poor agreement (R2 ranging from 0.22 to 0.04) with the observed rainfall but good agreement at monthly (R2 = 0.85) and annual scales (R2 = 0.80). The results showed that estimated accumulated precipitation overestimated the observed data. The results showed that R2 ranged from 0.51 to 0.55 at monthly scale and 0.44-0.52 at annual scale. However, the global data can represent well the variability of rainfall within the region. The results indicated a good correlation in the seasonal variability (R2 ranged from 0.72 to 0.60). The modeling results using monthly TRMM data and observed rainfall data showed good values of NS and R2 during calibration and validation, but PBIAS was unsatisfactory for the three streamflow gauges. The streamflow estimates from the SWAT model using data from the TRMM satellite showed that such data are capable of generating satisfactory results after calibration, although measured rainfall data presented better results; the data could support areas with scarce rainfall data and be applied to other river basins, for example, to analyze the hydrological potential of other basins in the coastal region of northeastern Brazil. Over the past three decades, considerable advances have been made in remote sensing with environmental satellites, increasing the amount of information available, including rainfall estimates. In this context, the use of TRMM data to estimate rainfall has ultimately been shown to be an interesting alternative for areas with scarce rainfall data.

A high-resolution daily gridded meteorological dataset for Serbia made by Random Forest Spatial Interpolation

We produced the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for 2000–2019, named MeteoSerbia1km. The dataset consists of five daily variables: maximum, minimum and mean temperature, mean sea-level pressure, and total precipitation. In addition to daily summaries, we produced monthly and annual summaries, and daily, monthly, and annual long-term means. Daily gridded data were interpolated using the Random Forest Spatial Interpolation methodology, based on using the nearest observations and distances to them as spatial covariates, together with environmental covariates to make a random forest model. The accuracy of the MeteoSerbia1km daily dataset was assessed using nested 5-fold leave-location-out cross-validation. All temperature variables and sea-level pressure showed high accuracy, although accuracy was lower for total precipitation, due to the discontinuity in its spatial distribution. MeteoSerbia1km was also compared with the E-OBS dataset with a coarser resolution: both datasets showed similar coarse-scale patterns for all daily meteorological variables, except for total precipitation. As a result of its high resolution, MeteoSerbia1km is suitable for further environmental analyses.

Probability of frost occurrence based on meteorological observation in Japan

We investigated meteorological conditions for frost occurrence using routine observation data during 1991–2010 from manned meteorological stations in Japan. The majority of the frost occurrence was observed when the daily minimum air temperature was in the range of − 5 °C to 5 °C. Due to strong radiative cooling of a frosted surface, frost was observed even when the daily minimum air temperature was above the dew-point temperature at a normal meteorological observation height. We proposed an analytical scheme to evaluate the probability of frost occurrence under given meteorological conditions. This scheme was based on a probability distribution of observed frost occurrence in a three-dimensional space of meteorological variables. The obtained distribution centered on − 1.9 °C for the daily minimum air temperature and 73% for the relative humidity, with an exponential decay with wind speed. The probability was halved when the daily minimum air temperature and relative humidity change by 3.3 °C and 24 percent-points, respectively, from the distribution center. Since this scheme gives probability of frost occurrence, it is advantageous for risk assessment under given meteorological conditions. Recent popularization of gridded meteorological datasets will help to expand the potential usage of the scheme.

A Long-Term, 1-km Resolution Daily Meteorological Dataset for Modeling and Mapping Permafrost in Canada

Climate warming is causing permafrost thaw and there is an urgent need to understand the spatial distribution of permafrost and its potential changes with climate. This study developed a long-term (1901–2100), 1-km resolution daily meteorological dataset (Met1km) for modeling and mapping permafrost at high spatial resolutions in Canada. Met1km includes eight climate variables (daily minimum, maximum, and mean air temperatures, precipitation, vapor pressure, wind speed, solar radiation, and downward longwave radiation) and is suitable to drive process-based permafrost and other land-surface models. Met1km was developed based on four coarser gridded meteorological datasets for the historical period. Future values were developed using the output of a new Canadian regional climate model under medium-low and high emission scenarios. These datasets were downscaled to 1-km resolution using the re-baselining method based on the WorldClim2 dataset as spatial templates. We assessed Met1km by comparing it to climate station observations across Canada and a gridded monthly anomaly time-series dataset. The accuracy of Met1km is similar to or better than the four coarser gridded datasets. The errors in long-term averages and average seasonal patterns are small. The error occurs mainly in day-to-day fluctuations, thus the error decreases significantly when averaged over 5 to 10 days. Met1km, as a data generating system, is relatively small in data volume, flexible to use, and easy to update when new or improved source datasets are available. The method can also be used to generate similar datasets for other regions, even for the entire global landmass.

Modeling Gross Primary Production of Midwestern US Maize and Soybean Croplands with Satellite and Gridded Weather Data

Gross primary production (GPP) is a useful metric for determining trends in the terrestrial carbon cycle. To estimate daily GPP, the cloud-adjusted light use efficiency model (LUEc) was developed by adapting a light use efficiency (LUE, ε) model to include in situ meteorological data and biophysical parameters. The LUEc uses four scalars to quantify the impacts of temperature, water stress, and phenology on ε. This study continues the original investigation in using the LUEc, originally limited to three AmeriFlux sites (US-Ne1, US-Ne2, and US-Ne3) by applying gridded meteorological data sets and remotely sensed green leaf area index (gLAI) to estimate daily GPP over a larger spatial extent. This was achieved by including data from four additional AmeriFlux locations in the U.S. Corn Belt for a total of seven locations. Results show an increase in error (RMSE = 3.5 g C m−2 d−1) over the original study in which in situ data were used (RMSE = 2.6 g C m−2 d−1). This is attributed to poor representation of gridded weather inputs (vapor pressure and incoming solar radiation) and application of gLAI algorithms to sites in Iowa, Minnesota, and Illinois, calibrated using data from Nebraska sites only, as well as uncertainty due to climatic variation. Despite these constraints, the study showed good correlation between measured and LUEc-modeled GPP (R2 = 0.80 and RMSE of 3.5 g C m−2 d−1). The decrease in model accuracy is somewhat offset by the ability to function with gridded weather datasets and remotely sensed biophysical data. The level of acceptable error is dependent upon the scope and objectives of the research at hand; nevertheless, the approach holds promise in developing regional daily estimates of GPP.

Evaluating the Suitability of Century-Long Gridded Meteorological Datasets for Hydrological Modeling

AbstractRecent advances in climate reanalyses have led to the development of meteorological products providing information from the beginning of the last century or even before. As these data sources might be of interest to practitioners in the event of missing data from meteorological stations, it is important to assess their usefulness for different applications. The main objective of this study is to investigate the ability of two long-term reanalysis datasets (CERA-20C and 20CR) and one long-term interpolated dataset (Livneh) for supporting hydrological modeling. The precipitation and temperature data of the three datasets were first compared, downscaled, and then used as inputs to the conceptual hydrological model HBV in 168 basins in the United States. The findings suggest that the quality of all three datasets decreases the further we go back in time. Models calibrated at the beginning of the time series, where the data quality is worse, are only able to capture the general properties of the time series and thus do not show a decrease in performance as the period between calibration and validation becomes larger. The opposite is true for models calibrated at the end of the time series, which show a clear decrease in performance toward the beginning of the century. While the hydrological model driven with the interpolated datasets achieved the best performance, the results obtained with the reanalysis datasets were still informative (i.e., better than the long-term monthly mean), and they matched the performance of the interpolated dataset in a few catchments in the northwestern United States.

Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 over the Qinghai-Tibet Plateau.

A bias-corrected dataset containing daily meteorological data over the Qinghai-Tibet Plateau has been generated using a trend-preserving bias correction, the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) approach, with a high-quality gridded meteorological dataset based on ground observations (CN05.1). The dataset contains daily bias-corrected values of maximum/minimum near-surface air temperature, precipitation and mean near-surface wind speed from 15 models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) and a downscaled high-resolution dataset (NEX-GDDP), based on CMIP5 models, over the Qinghai-Tibet Plateau (QTP) during 1986–2095. This dataset provides an important reference for the study of future climate change and its impacts in the Qinghai-Tibet Plateau region.