In the last few years, our research efforts have been directed to the following areas: With general circulation models having spatial resolution too coarse to reveal climate variability at local scales, ML methods have been developed to nonlinearly downscale the model output to finer spatial scales, especially for precipitation and streamflow. Machine learning methods are ideal for extracting information from satellite data. Crop yield prediction models have been developed by applying ML methods to vegetation indices derived from satellite data. ML methods have been used to improve forecasts of air quality over Canadian cities. ML methods have been used in data fusion, i.e. combining various gridded products, to improve estimates of snow depth (i.e. snow water equivalent) over British Columbia. While ML methods such as artificial neural networks are able to extract nonlinear signals missed by linear statistical methods, they are computationally much more expensive. We have been developing new ML models which are several orders of magnitude faster than the standard ML models, especially when the models need to be updated frequently as new data arrive continually (i.e. online learning).