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Introduction: With global warming, the disaster losses caused by extreme precipitation events are increasing. The poor natural conditions and climate change make the arid and semi-arid mountainous grassland area a sensitive region of climate change. The study on the spatio-temporal variation characteristics of extreme precipitation events in this region is helpful to improve the ability of climate prediction and disaster prevention and reduction in grassland.Methods: Based on the daily precipitation data of four meteorological monitoring stations in the Yinshanbeilu from 1970 to 2020, the trend analysis, M-K test and wavelet analysis were used to select seven typical extreme precipitation indicators to analyze the temporal and spatial characteristics of extreme precipitation.Results and Discussion: The results showed that the precipitation in the Yinshanbeilu increased in the past 51a, and the number of heavy rain days increased significantly. The significance test of CDD and CWD showed that the number of continuous dry days and continuous wet days decreased abruptly. The spatial analysis showed that the high value areas of R95p, R95d and PRCRTOT were all located in Siziwang Banner, and it could be concluded that the extreme precipitation risk was the highest in Siziwang Banner, while the low value areas of SDII, Rx1day, R95p and PRCRTOT were all located in Sonid Right Banner, which could be inferred that the Sonid Right Banner was relatively dry. The first main cycle of the seven indexes of extreme precipitation almost runs through the whole time series, and the starting point of the minimum main cycle changes inconsistent. In addition to the number of consecutive dry days, the other indices have a good correlation with annual precipitation, flood season and monthly precipitation from June to September, and July is the peak period of extreme precipitation events.

This research offers a new perceptive of the dynamic connection between CO2 and globalization, technological innovation (TI), economic growth, and renewable energy (REN) consumption in Spain. The current research applied the wavelet tools to assess these interconnection utilizing data stretching from 1980 to 2018. The outcomes of these analyses disclose that the association among the series evolves over frequency and time. The current analysis uncovers notable wavelet coherence and strong lead and lag connections in the frequency domain, whereas the time domain indicates inconsistent correlations among the variables of interest. The outcomes of the wavelet analysis from the economic perspective affirm that in the short and medium term, globalization, economic growth, and TI contribute to environmental degradation; however, in the long term, globalization, economic growth, and TI do not contribute to the degradation of the environment. Moreover, at different frequencies, REN consumption helps mitigate environmental degradation in Spain. Furthermore, the spectral causality test unveiled that in the long term, REN utilization, globalization, TI, and economic growth can predict CO2 emissions in Spain.