Seasonal climate prediction offers the potential to anticipate variations in crop production
early enough to adjust critical decisions. Until recently, interest in exploiting seasonal …

The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a major
international effort linking the climate, crop, and economic modeling communities with …

A statistical bias correction methodology for global climate simulations is developed and
applied to daily land precipitation and mean, minimum and maximum daily land …

Shifts in rainfall and rising temperatures due to climate change pose a formidable challenge
to the sustainability of broadacre crop yields in Western and South-Eastern Australia. Output …

This review discusses the challenges for integrating seasonal climate forecast information in
user applications within the design of a simplified end-to-end forecasting system framework …

Statistical downscaling (SD) derives localized information from larger-scale numerical
models. Convolutional neural networks (CNNs) have learning and generalization abilities …

Background Climate change is expected to significantly impact agricultural production
across Africa. While a number of studies assessed this impact in semi-arid southern Africa …

North Korea is a food-deficit nation in which climate change could have a significant impact
on drought. We analyzed drought characteristics in the Hwanghae Plain, North Korea using …

Precipitation estimation is an important and challenging task in hydrology because of high
variability and changing climate. This research involves (1) analyzing changes (trend and …

A daily stochastic spatiotemporal precipitation generator that yields precipitation realizations
that are quantitatively consistent is described. The methodology relies on a latent Gaussian …