  `scores` is a Python package containing mathematical functions for the
verification, evaluation and optimisation of forecasts, predictions or models.
It supports labelled n-dimensional (multidimensional) data, which is used in
many scientific fields and in machine learning. At present, `scores` primarily
supports the geoscience communities; in particular, the meteorological,
climatological and oceanographic communities. `scores` not only includes common
scores (e.g., Mean Absolute Error), it also includes novel scores not commonly
found elsewhere (e.g., FIxed Risk Multicategorical (FIRM) score, Flip-Flop
Index), complex scores (e.g., threshold-weighted continuous ranked probability
score), and statistical tests (such as the Diebold Mariano test). It also
contains isotonic regression which is becoming an increasingly important tool
in forecast verification and can be used to generate stable reliability
diagrams. Additionally, it provides pre-processing tools for preparing data for
scores in a variety of formats including cumulative distribution functions
(CDF). At the time of writing, `scores` includes over 50 metrics, statistical
techniques and data processing tools. All of the scores and statistical
techniques in this package have undergone a thorough scientific and software
review. Every score has a companion Jupyter Notebook tutorial that demonstrates
its use in practice. `scores` supports `xarray` datatypes, allowing it to work
with Earth system data in a range of formats including NetCDF4, HDF5, Zarr and
GRIB among others. `scores` uses Dask for scaling and performance. Support for
`pandas` is being introduced. The `scores` software repository can be found at
https://github.com/nci/scores/
