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Soil temperature is a very important variable in agricultural meteorology and strongly influences agricultural activities and planning (e.g. the date and depth of sowing crops, frost protection). There are many physically based studies in the literature which model soil temperature, but few are easily applicable for use in the field. Simple and precise short-term forecasting of soil temperature with minimum data requirements is the main goal of this study. The soil temperature at 0300, 0900 and 1500 GMT was forecast based only on surface air temperatures using artificial neural network (ANN) and wavelet transform artificial neural network (WANN) models. The hourly data were collected from the Mashhad synoptic station in Khorasan Razavi province in Iran between 2010 and 2013. The results of this study showed that using a wavelet transform for preprocessing improved the accuracy of soil temperature forecasting. It was also found that changing the temporal increment in forecasting time did not have a noticeable effect on errors in the WANN models. WANN models can be used as accurate tools to forecast soil temperature 1–7 days ahead at depths of 5–30 cm.

Large-scale climatic teleconnections have noticeable effects on meteorological events in different regions of the world. In this study, the linkages between three major climatic indices, Arctic Oscillation (AO), North Atlantic Oscillation (NAO) and Southern Oscillation Index (SOI), and precipitation in Iran were assessed from 1960 to 2014, at 30 synoptic stations in a time-frequency space, using wavelet coherence (WCO). The results showed that the SOI is the most effective climatic teleconnection on precipitation in Iran, although the other studied climatic indices have noticeable effects as well. The predominant and effective period of AO on precipitation was equal to or greater than 32 months at most of the stations, while the major effective period of NAO was equal to or greater than 64 months. For the SOI, most parts of the country were affected by a period of less than 64 months, while the predominant period of SOI for the northwestern part of the country was greater than 64 months. A uniform phase difference was not observed between the three studied climatic indices and precipitation in the country; instead the phase differences were usually random. For long-term periods of SOI, an anti-phase situation was detected at most of the stations. The study suggested that the WCO is a very powerful and flexible method for studying the relationship between multiple time series in a time–frequency space, and its application in hydrological and meteorological research is expected to increase in the near future.