Extreme events such as heat waves and cold spells, droughts, heavy rain, and storms are
particularly challenging to predict accurately due to their rarity and chaotic nature, and …

Despite the increasingly successful application of neural networks to many problems in the
geosciences, their complex and nonlinear structure makes the interpretation of their …

Scientific and technological advances in numerical modelling have improved the quality of
climate predictions over recent decades, but predictive skill remains limited in many aspects …

Climate-driven changes in precipitation amounts and their seasonal variability are expected
in many continental-scale regions during the remainder of the 21st century. However, much …

In recent years, artificial intelligence and specifically artificial neural networks (NNs) have
shown great success in solving complex, nonlinear problems in earth sciences. Despite their …

A barrier to utilizing machine learning in seasonal forecasting applications is the limited
sample size of observational data for model training. To circumvent this issue, here we …

Classifying the state of the atmosphere into a finite number of large-scale circulation regimes
is a popular way of investigating teleconnections, the predictability of severe weather events …

As opaque predictive models increasingly impact many areas of modern life, interest in
quantifying the importance of a given input variable for making a specific prediction has …

Sub-seasonal forecasting (SSF) is the prediction of key climate variables such as
temperature and precipitation on the 2-week to 2-month time horizon. Skillful SSF would …

Accurate and reliable seasonal forecasts are important for water and energy supply
management. Recognizing the important role of snow water equivalent (SWE) for water …