Abstract:

The homogeneity of the temperature series is fundamental to the accuracy of climate change assessments. To enhance the reliability of temperature records in Shandong province, monthly mean, maximum and minimum temperature data from 123 national basic meteorological stations up to 2023 were compiled. A combined detection framework that integrates the Penalized maximum F−test (PMF), Penalized maximum T−test (PMT) and Standard normal homogeneity test (SNHT), supplemented by comprehensive metadata, was applied to perform systematic homogeneity testing and adjustment. The results showed that: (1) inhomogeneity proved spatially ubiquitous with 63, 71 and 114 statistically significant breakpoints were identified in the monthly mean, maximum and minimum temperature series, respectively. Station relocation was the dominant driver of these discontinuities. (2) After applying a one−segment linear regression adjustment, the warming trend for annual mean maximum temperature increased from 0.230℃·10y⁻¹ to 0.260℃·10y⁻¹, while the trend for annual mean minimum temperature rose from 0.330℃·10y⁻¹ to 0.400℃·10y⁻¹. (3) Homogenization markedly improved the continuity and homogeneity of individual station records, yet it did not alter the overall warming pattern across Shandong, only subtle spatial differences emerged, most notably an intensifying warming trend in the southeast of Shandong, which was attributed to station relocation. The homogenized dataset developed in this study provides a robust scientific basis for more precise evaluation of climate change risks and impacts in Shandong province.

Key words: Shandong, Temperature series, Homogeneity test, Trend analysis, Correction, RHtest software