Daily precipitation of 55 meteorological stations over the Hanjiang River basin was regard as stochastic signal in order to express its change characteristics on temporal scale.At first

daily precipitation signal was decomposed using the Haar wavelet

the Daubechies(db2) wavelet

etc.and the wavelet coefficient from decomposition of daily precipitation was regionalized using the Kriging spatial interpolation algorithm.Then the wavelet coefficient in each 100 m×100 m grid was reconstructed by the Haar wavelet and six other wavelets to get the spatial and temporal mapping of daily precipitation and test the sensitivity of different wavelet functions to the interpolated results.Meanwhile the daily precipitation amount from other 45 meteorological stations over the Hanjiang River basin was used to validate the interpolated results quantitatively by five indexes:mean error(ME)

mean absolute error(MAE)

root mean-square error(RMSE)

correlation coefficient

and determinacy coefficient.Results show that the Haar wavelet is the best wavelet to decompose and reconstruct daily precipitation and express the temporal-spatial variation of daily precipitation.The wave profile of the Haar wavelet in the time domain resembles the shape of the precipitation impulses in a given time span

which might be the best way to simulate the temporal variation of daily precipitation.So the interpolated results of daily precipitation amount using the Haar wavelet is perfect in the spatial-temporal interpolation model based on wave over the Hanjiang River basin.