高分辨率中国CMIP6统计降尺度气候预估数据集1979-2100（HiCPC）

HiCPC (High-resolution CMIP6 downscaled daily Climate Projections over China) 数据集为清华大学地球系统科学系罗勇教授团队研制，由CMIP6全球模式经降尺度和误差校正后得到的中国范围的气候预估数据集，包括日降水、地表日平均和最高、最低气温4个变量，涵盖气候模式历史期（1979-2014）和未来（2015-2100） SSP1-2.6、SSP2-4.5、SSP3-7.0和SSP5-8.5四种情景下的模式降尺度结果。水平空间分辨率为0.1°，时间分辨率为1天（为方便使用，同时提供了逐月数据）。该数据集是以22个CMIP6全球模式为输入，采用改进的日BCSD方法对历史期和未来预测结果进行校正。历史观测资料来自于CMFD高分辨率历史气象数据集。校正后降水均值平均偏差小于0.1 mm/d,气温平均偏差小于0.4℃，较原始模式结果有较大改善。 HiCPC数据集未来将随着更多模型数据的完成而不断扩展，也将持续更新更多的变量（如近地面风、湿度、长短波辐射等）。该数据集为研究人员和政策制定者提供了更细致的空间尺度，帮助其了解未来潜在的气候变化。

HiCPC (High-resolution CMIP6 downscaled daily Climate Projections over China) dataset was developed by the team led by Professor Luo Yong from the Department of Earth System Science, Tsinghua University. It is a climate projection dataset covering China, generated via downscaling and error correction of outputs from CMIP6 global climate models, and includes four core variables: daily precipitation, daily surface mean, maximum and minimum air temperatures. The dataset provides downscaled model results for both the historical reference period (1979–2014) and future projection periods (2015–2100) under four Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. It has a horizontal spatial resolution of 0.1° and a daily temporal resolution; monthly aggregated datasets are also provided for user convenience. This dataset utilizes 22 CMIP6 global climate models as input, and applies an improved daily BCSD (Bias-Corrected Spatial Disaggregation) method to calibrate both historical and future prediction outputs. Historical observational data are sourced from the CMFD high-resolution historical meteorological dataset. After calibration, the average bias of precipitation is less than 0.1 mm/d, and the average bias of air temperature is less than 0.4°C, which represents substantial improvements over the original CMIP6 model outputs. The HiCPC dataset will be continuously expanded as more model data become available, and additional variables including near-surface wind, humidity, shortwave and longwave radiation will be added in future updates. This dataset provides finer spatial-resolution climate data for researchers and policymakers, aiding their investigations and analyses of potential future climate changes.

AI搜集汇总

背景与挑战

背景概述

该数据集是由清华大学团队基于CMIP6全球模式降尺度和误差校正生成的中国范围高分辨率气候预估数据，覆盖1979-2100年，包括日降水、气温等4个变量，空间分辨率为0.1°，提供日值和月值数据。它涵盖历史期和未来四种SSP情景，通过校正显著提高了精度，适用于气候变化研究和政策制定。