Two methods of linear trend estimation: the ordinary least squares (OLS, parametric) and Theil-Kendall (TK, nonparametric) are compared in the paper. The comparison was made using 65 time series of annual totals, Pa, and annual daily maximum, Pmax, of precipitation, 30-year long each, recorded in the south-eastern part of Poland (the Upper Vistula catchment). The OLS and TK slope coefficients of trends revealed high similarity for both Pa and Pmax series. The signs of slopes are the same for 64 sites for Pa and 63 sites for Pmax with positive signs prevailing: the numbers of decreasing trends for Pa OLS and TK slopes were 3 and 4, respectively, and, for Pmax, 13 for both OLS and TK slopes. In trend significance testing, both methods produced similar results for Pa time series: out of 16 significant trends, 13 were determined with both OLS and TK at the same sites. For Pmax series such agreement was found for 4 trends out of 10.
Spatial distribution of significant trends showed a kind of clustering in certain parts of the investigated area.