  Water managers in the western United States (U.S.) rely on longterm forecasts
of temperature and precipitation to prepare for droughts and other wet weather
extremes. To improve the accuracy of these longterm forecasts, the U.S. Bureau
of Reclamation and the National Oceanic and Atmospheric Administration (NOAA)
launched the Subseasonal Climate Forecast Rodeo, a year-long real-time
forecasting challenge in which participants aimed to skillfully predict
temperature and precipitation in the western U.S. two to four weeks and four to
six weeks in advance. Here we present and evaluate our machine learning
approach to the Rodeo and release our SubseasonalRodeo dataset, collected to
train and evaluate our forecasting system.
  Our system is an ensemble of two regression models. The first integrates the
diverse collection of meteorological measurements and dynamic model forecasts
in the SubseasonalRodeo dataset and prunes irrelevant predictors using a
customized multitask model selection procedure. The second uses only historical
measurements of the target variable (temperature or precipitation) and
introduces multitask nearest neighbor features into a weighted local linear
regression. Each model alone is significantly more accurate than the debiased
operational U.S. Climate Forecasting System (CFSv2), and our ensemble skill
exceeds that of the top Rodeo competitor for each target variable and forecast
horizon. Moreover, over 2011-2018, an ensemble of our regression models and
debiased CFSv2 improves debiased CFSv2 skill by 40-50% for temperature and
129-169% for precipitation. We hope that both our dataset and our methods will
help to advance the state of the art in subseasonal forecasting.
