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[1] Understanding and predicting regional impacts of El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) on winter (October-March) precipitation can provide valuable inputs to agricultural and water resources managers. Effects of ENSO and PDO on winter precipitation were assessed in 165 climate divisions throughout the southern United States. A continuous region of significantly (P < 0.05) increased (decreased) winter precipitation in response to El Niño (La Niña) conditions in the preceding summer (June-September Southern Oscillation Index (SOI)) extends across the entire southern United States and as far north as South Dakota. Within this region stronger correlations (r À0.45) are found along the Gulf of Mexico, southern Arizona, and central Nebraska. Winter precipitation differs significantly (P < 0.1) between warm and cold phase PDO periods only in the south central region, with greatest significance centered in Oklahoma. Enhanced negative La Niña anomalies during PDO cold phases are dominant in the central region (Texas to South Dakota) whereas enhanced positive El Niño anomalies during PDO warm phases are dominant in the southwest (Arizona, Nevada, and California) and southeast (Louisiana to Florida). Validation tests of winter precipitation predictions based on summer SOI and/or PDO-phase show a decrease of 9% to 16% in the relative Mean Absolute Error (MAE) from the MAE obtained by using the mean as a predictor in areas with strong correlation (r < À0.45) between SOI and precipitation. Logistic regression probability models of having above or below average winter precipitation had up to 77% successful predictions. The advantage of having probabilities of exceeding certain precipitation thresholds at the beginning of a hydrologic year makes logistic regression models attractive for decision makers.

Interpolating values of climate variables from measurement stations to large areas is important in a variety of disciplines. Each of the 38 climate observation stations in the Israel area represents an average area of 725 km 2 . Therefore it is important to minimize the extent of interpolation errors by using a suitable interpolation method. In this study we compared the performance of 2 local interpolation methods, Spline and Inverse Distance Weighting (IDW), with the performance of multiple regression models. These interpolation methods were applied to 4 temperature variables: mean daily temperature of the coldest month (January), mean daily temperature of the warmest month (August), the lowest mean monthly minimum temperature (January) and the highest mean monthly maximum temperature (June). Spline and IDW models with a range of parameter settings were applied to elevation detrended temperature data. The multiple regression models were based on geographic longitude, latitude and elevation and included terms of first and second order. Two methods of variable selection (Stepwise, Forced Entry) were used to construct 2 regression models for each temperature variable. Accuracy was assessed by a one-left-out cross validation test. Mean daily temperature variables proved more predictable than mean monthly extreme temperature variables. Mean daily temperature variables were predicted more accurately by using a regression model, whereas mean monthly extreme temperature variables were somewhat better predicted by a local interpolation method. The Spline interpolator predicted more accurately than IDW for the 2 summer temperature variables, while IDW performed better for the winter temperature variables. Combining multiple regression and local interpolation methods improved prediction accuracy by about 5% for the extreme temperature variables but did not effect the prediction of mean daily temperatures. Errors in the estimation increased with the use of local interpolation methods in areas where neighboring data were not 'local enough' to show micro-climatic influences. Where the data supported strong short-range climatic factors (such as the cooling effect of the Mediterranean Sea on the shoreline in summer), local methods were more effective than regression models, which became complicated and tended to over extrapolate. These findings suggest that in some instances simple overall regression models can be as effective as sophisticated local interpolation methods, especially when dealing with mean climatic fields. KEY WORDS: Climate variables · Interpolation methods · IsraelClim Res 13: 33-43, 1999 Matsuura 1995, Dodson & Marks 1997), splines (Hulme et al. 1995) and kriging (Holdaway 1996, Hudson & Wackernagel 1994, Hammond & Yarie 1996. It is also common to many of these studies that they use variables such as elevation, latitude and longitude as predictors of climatic variables. Some authors use methods where trends lie within the interpolator, for example the various versions of Universal Kriging (Huds...

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