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\begin{document}

\title{A Process Driven Downscaling Technique to Improve Confidence in Climate
Projections}



\author[1]{Jose George}%
\author[1]{Athira P}%
\affil[1]{Indian Institute of Technology Palakkad}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\begin{abstract}
Statistical downscaling plays an important role in reducing the
uncertainty in regional-scale climate change studies. A major assumption
of all statistical downscaling studies is the stationarity in the
relationship identified from the historical observations. This
assumption is difficult to validate since the future is truly unknown.
The proposed methodology tries to overcome the limitations of this
assumption by considering global physics that drive the regional climate
to select the predictors to develop the statistical relationship. Since
the statistical model is developed with climatic processes as a
background, it can be said with higher confidence that the modelled
relation would remain sound for the future as well. The proposed
methodology is divided into two stages. In the first stage, a Relevance
Vector Machine model is developed using historical observed global
predictors that are identified to have teleconnections with the regional
climate predictand. The spatial downscaling of this regional predictand
has been done on a monthly scale. The bias associated with the
downscaled predictand is removed by separating the anomalies from the
prediction and adding that to the historical mean of the observed data.
This monthly series is further disaggregated into daily series using a
weather generator. The non-stationarity in the climate projections is
accommodated in the weather generator and more regional features of the
climate is integrated into the predictand in this stage. The proposed
methodology is validated by downscaling rainfall over a river basin in
India and its performance is analysed. The downscaled data is seen to
reproduce characteristics of daily rainfall like consecutive wet days,
the number of rainy days, wet spell duration, and dry spell duration
with a bias of less than 10\%.%
\end{abstract}%



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