Analyzing trend and prediction of precipitation in
Germany using non-parametrical tests and machine
learning algorithms

The study explores analyzes the temporal changes in precipitation using the data from 1881 to 2020 across Germany at the regional level. Man-Kendall and Hamad-Rao modification tests were employed to analyze the precipitation trend,while Pettit test was used for detecting the change point in the time frame. Machine learning methods like k-nearest neighbour, Support vector machine and Random forest algorithms were applied for prediction. Most of the regions showed an increasing trend annually and seasonally in 0.05 significance level while some negative can be seen in summer. Furthermore, Based on Pettit test, most of the change points were detected after 1940 in several regions. In the prediction of precipitation, k-NN algorithm showed better performance in terms of mean absolute error rather than Support vector machine and Random forest algorithms.