The Carpathians are the longest mountain range in Europe and a geographic barrier between Central Europe, Eastern Europe, and the Balkans. To investigate the climate of the area, the CARPATCLIM project members collected, quality-checked, homogenized, harmonized, and interpolated daily data for 16 meteorological variables and many derived indicators related to the period 1961–2010. The principal outcome of the project is the Climate Atlas of the Carpathian Region, hosted on a dedicated website (www.carpatclim-eu.org) and made of high-resolution daily grids (0.1° × 0.1°) of all variables and indicators at different time steps. In this article, we analyze the spatial and temporal variability of 10 variables: minimum, mean, and maximum temperature, daily temperature range, precipitation, cloud cover, relative sunshine duration, relative humidity, surface air pressure, and wind speed at 2 m. For each variable, we present the gridded climatologies for the period 1961–2010 and discuss the linear trends both on an annual and seasonal basis. Temperature was found to increase in every season, in particular in the last three decades, confirming the trends occurring in Europe; wind speed decreased in every season; cloud cover and relative humidity decreased in spring, summer, and winter, and increased in autumn, while relative sunshine duration behaved in the opposite way; precipitation and surface air pressure showed no significant trend, though they increased slightly on an annual basis. We also discuss the correlation between the variables and we highlight that in the Carpathian Region positive and negative sunshine duration anomalies are highly correlated to the corresponding temperature anomalies during the global dimming (1960s and 1970s) and brightening (1990s and 2000s) periods.

This study updates knowledge on climate evolution in Madagascar from 1950 to 2018. Changes were analyzed using annual and seasonal climate indices at regional and station level. The original daily series of minimum and maximum temperature and precipitation obtained from 28 meteorological stations were quality controlled and homogenized. Thirty‐seven (37) climate indices were obtained from the daily series. The results show that changes in temperature had a higher degree of spatial coherence than changes in precipitation. Trends for temperature indices were mostly significant at 0.05 level and compatible with warming. Changes in minimum temperatures were greater than those for the maximum, leading to a significant decrease in the diurnal temperature range (DTR). Warm nights increased more than warm days, (0.70 days⋅decade^–1) and cold nights decreased more than cold days, (0.21 days⋅decade^–1). In addition, we found more stations with significant trends for very cold nights (92.60%) than for very warm days (51.80%) but they progressed differently (decrease and increase, respectively). Station‐by‐station precipitation index trends were mostly non‐significant at 0.05 level, and most regional precipitation index showed decreasing trends. A shift in precipitation magnitude was observed around 2000–2018, a period of intensified drying (where 70.40% of stations recorded non‐significant decreasing trends). An analysis of drought characteristics (i.e., intensity, magnitude and duration) highlighted the situation, especially in the south‐east at an annual timescale.

In the paper, we present a dataset of long gridded time series of monthly minimum, maximum and mean air temperature and atmospheric precipitation for Ukraine (one of the largest European countries), covering the period of 1946–2020. The dataset was built through the thorough historical climate data processing, which included all mandatory steps: data rescue/digitization of missing values and/or periods in station time series from paper sources, their quality control (QC) and homogenization, and finally interpolation on 0.1^o × 0.1^o grid. The station data comprised monthly values of 178 stations for air temperature (for each of three parameters) and 224 stations for atmospheric precipitation. The quality assurance and homogenization were performed by means of the widely used homogenization software HOMER (HOMogEnization in R), while the well-known interpolation software MISH (Meteorological Interpolation based on Surface Homogenized data basis) was used to perform the gridding. The dataset has the potential to be used in a wide variety of applications, such as regional climate change and variability studies, assessment of other climate gridded products (e.g., reanalysis or analysis), global/regional climate model output verification, etc.

The widely used Global Historical Climatology Network (GHCN) monthly temperature dataset is available in two formats—non-homogenized and homogenized. Since 2011, this homogenized dataset has been updated almost daily by applying the “Pairwise Homogenization Algorithm” (PHA) to the non-homogenized datasets. Previous studies found that the PHA can perform well at correcting synthetic time series when certain artificial biases are introduced. However, its performance with real world data has been less well studied. Therefore, the homogenized GHCN datasets (Version 3 and 4) were downloaded almost daily over a 10-year period (2011–2021) yielding 3689 different updates to the datasets. The different breakpoints identified were analyzed for a set of stations from 24 European countries for which station history metadata were available. A remarkable inconsistency in the identified breakpoints (and hence adjustments applied) was revealed. Of the adjustments applied for GHCN Version 4, 64% (61% for Version 3) were identified on less than 25% of runs, while only 16% of the adjustments (21% for Version 3) were identified consistently for more than 75% of the runs. The consistency of PHA adjustments improved when the breakpoints corresponded to documented station history metadata events. However, only 19% of the breakpoints (18% for Version 3) were associated with a documented event within 1 year, and 67% (69% for Version 3) were not associated with any documented event. Therefore, while the PHA remains a useful tool in the community’s homogenization toolbox, many of the PHA adjustments applied to the homogenized GHCN dataset may have been spurious. Using station metadata to assess the reliability of PHA adjustments might potentially help to identify some of these spurious adjustments.

Digital databases of monthly means of daily extreme, minimum (Tn) and maximum (Tx), air temperature collected in Ukraine were created. The databases contain temperature records of 371/340 (Tn/Tx) climatological stations which recorded data on the territory of Ukraine anytime during 1881‐2015. The significant part of the datasets (~48/47%) was obtained after conducting a Data Rescue process, recovering historical paper records. Due to a great deal of missing data during World War I and II, homogenization was possible to perform only for 178 time series covering the period of 1946‐2015. A homogenization procedure, conducted by means of the HOMER software, revealed 323 break points for minimum temperature (~1.8 breaks per station) and 310 for maximum temperature (~1.7 breaks per station). Approximately 37/33% (Tn/Tx) of the breaks can be explained by metadata. Shift amplitudes in Tn time series ranged between ‐1.18^oC and 1.71^oC, while for maximum temperature the range was slightly less: from ‐0.99^oC to 1.15^oC. For both temperatures, mean values of shift amplitudes were near zero (‐0.04/‐0.05^oC). To validate the homogenization results, several statistical procedures were performed to compare inhomogeneous and homogeneous time series. The conducted statistical calculations provide the proofs that inhomogeneity of the Ukrainian extreme air temperature data should be taken into account when analyzing regional climate change and variability.