High-resolution future temperature and precipitation dataset for Canada, 2015 - 2100

A database of high-resolution (0.1°) precipitation, maximum, and minimum temperature projections extending till 2100 at a daily scale is developed for Canada. We employed a novel Semi-Parametric Quantile Mapping (SPQM) methodology to bias-correct the Coupled Model Intercomparison Project, Phase-6 (CMIP6) projections for four distinct Shared Socio-economic Pathways. SPQM is simple, yet robust, in reproducing the observed marginal properties, trends and variability according to future scenarios, and maintaining a smooth transition from observations to projected simulations. The database encompasses a substantial collection of 759 simulations derived from 37 diverse climate models for precipitation. Similarly, for maximum and minimum temperature projections, our database comprises 652 simulations from 30 climate models. These meticulously curated projections carry immense value for hydrological, environmental, and ecological studies, offering a comprehensive resource for analyses within these domains. Furthermore, these projections serve as a valuable asset for the quantification of uncertainties arising from variant labels, climate models, and future scenarios.

加拿大地区直至2100年每日尺度的高分辨率（0.1°）降水量、最高温度和最低温度预测数据库已构建完成。本研究采用了一种创新的半参数分位数映射（Semi-Parametric Quantile Mapping，简称SPQM）方法，对耦合模式比较计划（Coupled Model Intercomparison Project，简称CMIP6）针对四个不同共享社会经济路径（Shared Socio-economic Pathways）的预测进行了偏差校正。SPQM方法虽简明扼要，却具有强大的鲁棒性，能够根据未来情景复现观测到的边缘属性、趋势和变异性，并确保从观测数据到预测模拟的平滑过渡。该数据库汇聚了由37个不同气候模型生成的759个降水模拟数据，对于最高温度和最低温度预测，数据库包含了由30个气候模型生成的652个模拟数据。这些精心策划的预测数据对于水文、环境和生态研究具有极高的价值，为这些领域的分析提供了全面的信息资源。此外，这些预测数据亦成为量化由不同标签、气候模型和未来情景带来的不确定性的宝贵资产。