Climate change affects biodiversity in diverse ways, necessitating the exploration of multiple climate dimensions using standardized metrics. However, existing methods for quantifying these metrics are scattered and tools for comparing alternative climate change metrics on the same footing are lacking. To address this gap, we developed “climetrics” which is an extensible and reproducible R package to spatially quantify and explore multiple dimensions of climate change through a unified procedure. Six widely used climate change metrics are currently implemented, including 1) Standardized Local Anomalies; 2) Changes in Probabilities of Local Climate Extremes; 3) Changes in Areas of Analogous Climates; 4) Novel Climates; 5) Changes in Distances to Analogous Climates; and 6) Climate Change Velocity. For climate change velocity, three different algorithms are implemented and available within the package including; a) Distanced-based Velocity (“dVe”); b) Threshold-based Velocity (“ve”); and c) Gradient-based Velocity (“gVe”). The package also provides additional tools to calculate the monthly mean of climate variables over multiple years, to quantify and map the temporal trend (slope) of a given climate variable at the pixel level, and to classify and map Köppen-Geiger (KG) climate zones. The climetrics R package is seamlessly integrated with the rts package for efficient handling of raster time-series data. The functions in climetrics are designed to be user-friendly, making them suitable for less-experienced R users. Detailed comments and descriptions in their help pages and vignettes of the package facilitate further customization by advanced users. In summary, the climetrics R package offers a unified framework for quantifying various climate change metrics, making it a useful tool for characterizing multiple dimensions of climate change and exploring their spatiotemporal patterns.