Abstract:
The downscaling techniques have been regarded for a long time as an essential component of
assessing climate change for impact assessment studies. General circulation models (GCMs),
known as the climate models, are frequently used to examine the influence of climate change
on a coarse level. As a result, the data produced by GCMs is not primarily useful for making
predictions, therefore downscaling is employed to produce high-resolution results. In this
study, the downscaling models for the future projections of daily minimum temperature,
maximum temperature, and precipitation were constructed for ten stations of Punjab. The
three downscaling methods, namely, the Multivariate Multiple Linear Regression model
(MMLR), Support Vector Machine (SVM), and Artificial Neural Networks (ANN) were
utilized for the construction of models. The best-identified model was employed for future
projection of the predictands (minimum, maximum temperature, and precipitation) using
the Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs, under two future
scenarios Shared Socioeconomic Pathways 4.5 (SSP 4.5) and SSP8.5 for the time period
2015-2045. The performance of each model was assessed graphically and numerically. For
numerical evaluation, four statistical performance indices were used including the Root
Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Mean Bias Error (MBE), and
Difference (D) in the variations between the observed and model predicted data. Meanwhile,
for graphical evaluation, monthly box plot and the Taylor diagram were used. The evaluation
results suggest that ANN outperforms the other two methods. Therefore, the best-identified
ANN model is then used to project the future minimum temperature, maximum temperature,
