Deep learning techniques (in particular convolutional neural networks, CNNs) have recently
emerged as a promising approach for statistical downscaling due to their ability to learn …

General circulation models (GCMs) allow the analysis of potential changes in the climate
system under different emissions scenarios. However, their spatial resolution is too coarse to …

Global climate models (GCMs) are the primary tools to simulate multi-decadal climate
dynamics and to generate global climate change projections under different future emission …

Satellite precipitation estimates (SPEs) have been widely used in various applications.
However, when applied to small basins and regions, the spatial resolution of SPEs is too …

In this paper, we evaluate the performance of 8 statistical and machine learning methods,
driven by atmospheric synoptic patterns, for long-term daily rainfall prediction in a semi-arid …

Statistical downscaling and dynamical downscaling are two approaches to generate high‐
resolution regional climate models based on the large‐scale information from either …

Despite the sophistication of global climate models (GCMs), their coarse spatial resolution
limits their ability to resolve important aspects of climate variability and change at the local …

We developed an updated nonstationary bias‐correction method for a monthly global
climate model of temperature and precipitation. The proposed method combines two widely …

The delivery of downscaled climate information is increasingly seen as a vehicle of climate
services, a driver for impacts studies and adaptation decisions, and for informing policy …

Seasonal mean rainfall projections for Hawai 'i are given based on statistical downscaling of
the latest Coupled Model Intercomparison Project phase 5 (CMIP5) global model results for …