  Climate change is one of the most critical challenges that our planet is
facing today. Rising global temperatures are already bringing noticeable
changes to Earth's weather and climate patterns with an increased frequency of
unpredictable and extreme weather events. Future projections for climate change
research are based on Earth System Models (ESMs), the computer models that
simulate the Earth's climate system. ESMs provide a framework to integrate
various physical systems, but their output is bound by the enormous
computational resources required for running and archiving higher-resolution
simulations. For a given resource budget, the ESMs are generally run on a
coarser grid, followed by a computationally lighter $downscaling$ process to
obtain a finer-resolution output. In this work, we present a deep-learning
model for downscaling ESM simulation data that does not require high-resolution
ground truth data for model optimization. This is realized by leveraging
salient data distribution patterns and the hidden dependencies between weather
variables for an $\textit{individual}$ data point at $\textit{runtime}$.
Extensive evaluation with $2$x, $3$x, and $4$x scaling factors demonstrates
that the proposed model consistently obtains superior performance over that of
various baselines. The improved downscaling performance and no dependence on
high-resolution ground truth data make the proposed method a valuable tool for
climate research and mark it as a promising direction for future research.
