Large-scale climate patterns are known to influence climatology as well as hydrology in various regions of the world. Several studies have been performed to understand the relationships between indices representing such patterns, usually in the form of anomalies and different climatic and hydrologic variables. These indices can predict future values of such variables. However, only a few recent studies have been published for the territory of Czechia in Central Europe that would contribute to their better usefulness during forecasts. Making use of a gridded dataset with a cell size of 500 m describing the evolution of daily precipitation totals and air temperature averages in Czechia between January 1961 and March 2021, the present study seeks the maximum absolute value of a nonparametric cross-correlation coefficient between monthly cell values and the monthly values of four selected climate indices: AMO, AO, NAO and SOI, with particular focus on the correlation coefficients at lags, allowing potential prediction. Individual raster layers were created for each index, showing the spatial distribution of the maximum cross-correlation and accompanying time lag. It was found that for the majority of Czechia, one can construct prediction models based on leading values of climate indices. Only very few raster cells do not show a significant correlation at the level of 0.05.

The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)