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Air temperature is the main climatic variable for climate change and global warming assessment. In different regions, different climatic features and forcing factors affect air temperature behaviour and development differently. In this paper, time series of monthly and annual absolute minimum, mean and absolute maximum air temperatures measured in the period Jan. 1949–Dec. 2018 at two meteorological stations, Zagreb Grič and Lastovo, were analysed. The stations are situated in distinct climatological areas in geographically and climatologically completely opposite parts of Croatia. The climate of Zagreb Grič station is mild continental. Station Lastovo is located on the small isolated Adriatic Sea island, which has a hot Mediterranean climate. The distance by air between the two analysed meteorological stations is 340 km. In time series of the absolute annual minimum air temperatures measured at both analysed stations, an increasing trend is not statistically significant, while in the time series of mean annual and absolute annual maximum air temperatures, the trends are statistically significant at the level p<0.01. The statistically significant trend of air temperature increase at both stations appeared in the warm season of the year generally from May to August. The day-to-day (DTD) method established a decrease in night-time to night-time air temperature variability, and an increase in the day-time to day-time temperature variability at Lastovo. In Zagreb Grič, the situation is the opposite but the process is very slow. [ABSTRACT FROM AUTHOR]

The Standardized Precipitation Index (SPI) for different time scales of n = 1, 2, 3, 6, 9, 12, and 24 months and the monthly Palmer Drought Severity Index (PDSI) were calculated for the Zagreb-Grič Observatory, located in North-Western Croatia, for the period 1862–2012. The PDSI exhibits a stronger long-term negative trend than the SPI due to the influence of the global warming, which corresponds with the global scale results published in the 4th IPCC (Intergovernmental Panel on Climate Change) report. The SPI for a 9-month scale was compared with the PDSI for a monthly scale because the correlation between them is the highest in comparison with other SPI time scales. The Chapman percentile classification regarding dryness/wetness severity was used instead of the "originally" proposed classes. Thirty-year moving averages indicate that long-term variation in dryness/wetness severity is more clearly emphasised for the PDSI than for the SPI, showing the last 30-year period to be the driest on record. Autoregressive function analysis indicates that the SPI for a 1-month scale has serialy independent values, while the SPI for the 9-month scale and the monthly PDSI are close to a Markov process. A significant correlation was established between the SPI and PDSI indices and crop damages in Croatia. [ABSTRACT FROM AUTHOR]