  A nonanticipative analog method is used for the long-term forecast of air
temperature extremes. The data to be used for prediction include average daily
air temperature, mean visibility, mean wind speed, mean dew point, maximum and
minimum temperatures reported during the day from 66 places around the world,
as well as sea level, average monthly Darwin and Tahiti sea level pressures,
SOI, equatorial SOI, sea surface temperature, and multivariate ENSO index.
Every dataset is split into two samples - learning (1973-2010) and validation
(2011-2013). Initially, the sum of variables in datasets for two locations,
minus corresponding climatological values, is calculated over a summation
interval of length from 1 to 365 days. A "quality criterion" selects datasets
for two locations with appropriate lead-time and summation interval, which have
maximum (or minimum) sum compared with the rest of data four times at least,
when extreme events occur later within the learning sample. Up to 18.2% of all
extremes are specifically predicted. The methodology has 100% accuracy with
respect to the sign of predicted and actual values. It is more useful than
current methods for predicting extreme values because it does not require the
estimation of a probability distribution from scarce observations.
