CRU TS (Climatic Research Unit gridded Time Series) is a widely used high-resolution climate dataset, covering all land domains except Antarctica, with a 0.5° latitude by 0.5° longitude grid. The dataset, now in its fourth version (CRU TS v4), spans from 1901 to 2018 and is updated annually. CRU TS v4 uses angular-distance weighting (ADW) for interpolation, improving traceability and diagnostic capabilities. The dataset includes ten observed and derived variables, with no missing values in the defined domain. Individual station series are normalized using 1961–1990 observations and then gridded to a 0.5° grid using ADW. The resulting anomaly grids are converted to actual values using CRU CL v1.0 climatologies. CRU TS has been widely used in various research areas, including localized weather and climate-dependent models, paleoclimate reconstructions, climate variability analysis, and bias correction for global and regional climate models and reanalyses. The dataset is available in NetCDF and space-separated ASCII text formats, with metadata indicating the level of station support for each datum. Cross-validation exercises demonstrate the accuracy of the ADW interpolation scheme, and comparisons with other datasets validate the quality of CRU TS v4.CRU TS (Climatic Research Unit gridded Time Series) is a widely used high-resolution climate dataset, covering all land domains except Antarctica, with a 0.5° latitude by 0.5° longitude grid. The dataset, now in its fourth version (CRU TS v4), spans from 1901 to 2018 and is updated annually. CRU TS v4 uses angular-distance weighting (ADW) for interpolation, improving traceability and diagnostic capabilities. The dataset includes ten observed and derived variables, with no missing values in the defined domain. Individual station series are normalized using 1961–1990 observations and then gridded to a 0.5° grid using ADW. The resulting anomaly grids are converted to actual values using CRU CL v1.0 climatologies. CRU TS has been widely used in various research areas, including localized weather and climate-dependent models, paleoclimate reconstructions, climate variability analysis, and bias correction for global and regional climate models and reanalyses. The dataset is available in NetCDF and space-separated ASCII text formats, with metadata indicating the level of station support for each datum. Cross-validation exercises demonstrate the accuracy of the ADW interpolation scheme, and comparisons with other datasets validate the quality of CRU TS v4.