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\begin{document}

\title{Scikit-downscale: an open source Python package for scalable climate
downscaling}



\author[1]{Joseph Hamman}%
\author[1]{Julia Kent}%
\affil[1]{National Center for Atmospheric Research}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\begin{abstract}
Climate data from Earth System Models are increasingly being used to
study the impacts of climate change on a broad range of biogeophysical
(forest fires, fisheries, etc.) and human systems (reservoir operations,
urban heat waves, etc.). Before this data can be used to study many of
these systems, post-processing steps commonly referred to as bias
correction and statistical downscaling must be performed. ``Bias
correction'' is used to correct persistent biases in climate model
output and ``statistical downscaling'' is used to increase the
spatiotemporal resolution of the model output (i.e. 1 deg to 1/16th deg
grid boxes). For our purposes, we'll refer to both parts as
``downscaling''. In the past few decades, the applications community has
developed a plethora of downscaling methods. Many of these methods are
ad-hoc collections of post processing routines while others target very
specific applications. The proliferation of downscaling methods has left
the climate applications community with an overwhelming body of research
to sort through without much in the form of synthesis guiding method
selection or applicability. Motivated by the pressing
socio-environmental challenges of climate change -- and with the
learnings from previous downscaling efforts in mind -- we have begun
working on a community-centered open framework for climate downscaling:
scikit-downscale. We believe that the community will benefit from the
presence of a well-designed open source downscaling toolbox with
standard interfaces alongside a repository of benchmark data to test and
evaluate new and existing downscaling methods. In this notebook, we
provide an overview of the scikit-downscale project, detailing how it
can be used to downscale a range of surface climate variables such as
air temperature and precipitation. We also highlight how
scikit-downscale framework is being used to compare existing methods and
how it can be extended to support the development of new downscaling
methods.%
\end{abstract}%



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