TY  - Data
T1  - Long-term daily composite temperature dataset for the Loess Plateau from 2015 to 2100
A1  - ZHANG Baoqing 
DO  - 10.12072/ncdc.loess.db7331.2026
PY  - 2026
DA  - 2026-05-21
PB  - National Cryosphere Desert Data Center
AB  - Temperature time series data serve as critical foundational information for global and regional climate research, hydrological modeling, and ecosystem assessments. However, despite the availability of various existing air temperature data products, the evaluation of long-term temperature dynamics and trend changes over the Loess Plateau remains subject to a certain degree of uncertainty. This is primarily due to challenges such as uneven distribution of observation networks, systematic biases between reanalysis data and measured values, and the lack of consistent spatial coverage and high-resolution continuity in long-term data series. To address these limitations, this study leverages future precipitation datasets from ACCESS-ESM1-5, IPSL-CM6A-LR, and MIROC-ES2L, constructing a downscaling model based on the U-Net deep learning architecture. The model is used to generate a future air temperature data product for the Loess Plateau covering the period 2015–2100. The spatial coverage of the dataset spans from 33.75°N to 41.25°N and 100.95°E to 114.55°E, with a high spatial resolution of 0.1° and daily temporal resolution. Compared to the original model data, the core advantage of this dataset lies in its ability to provide high-resolution temperature data that more finely captures the spatio-temporal distribution of air temperature. This significantly enhances the simulation of localized extreme high and low temperature events, thereby offering more reliable data support for regional climate modeling, hydrological process analysis, and studies of extreme temperature events.The data is stored in NetCDF format (*.nc). The data files are named using the format: LoessPlateauRegion_ACCESS-ESM1-5_ssp126_temp_max_2015_2100_merged. In this naming convention, LoessPlateauRegion denotes the regional name, ACCESS-ESM1-5 specifies the climate model name, and ssp126 indicates the future scenario. Within the data files, the variable temp_min represents daily minimum temperature, an
DB  - NCDC
UR  - http://www.ncdc.ac.cn/portal/metadata/9b6d4fec-a5b3-4ec7-8bf9-1cd85eeb5ab9
ER  -