Publication details
Is the Quitt climate classification still relevant? Challenges posed by recent climate change
| Authors | |
|---|---|
| Year of publication | 2026 |
| Type | Peer-reviewed scientific article |
| Magazine / Source | Theoretical and Applied Climatology |
| MU Faculty or unit | |
| Citation | |
| web | https://link.springer.com/article/10.1007/s00704-026-06681-2 |
| Doi | https://doi.org/10.1007/s00704-026-06681-2 |
| Keywords | Quitt climate classification; normal periods; Czech Republic; climate change |
| Description | Climate classifications based on fixed threshold values are increasingly challenged under recent climate change, yet remain broadly used. Accordingly, this contribution assesses the relevance of the widely used Quitt climate classification for the Czech Republic as a representative region of Central Europe. The original methodology, derived from the 1901–1950 period, was re-applied to a gridded dataset of multiple climate variables based on homogenised station series for the 1961–1990 and 1991–2020 normal periods to produce an updated version of the Quitt climate classification. For the 1991–2020 period, results indicate a significant expansion of the Warm group of climatic regions (by 30%) at the expense of the Moderately Warm and Cold groups. Following these spatial shifts, the Updated Quitt climate classification lost its explanatory power during this period. The original classification criterion (matching ? 12 of 14 variables) was met in only 5% of the Czech Republic due to a loss of internal consistency between temperature and precipitation characteristics: rising temperatures shifted climatic regions to higher elevations, while precipitation totals remained stable. The results of this study unequivocally demonstrate that the Updated Quitt climate classification is no longer representative for the 1991–2020 period. Moreover, the unsuitability of the original Quitt classification method based on fixed threshold values emphasises the need to create a new climate classification that better reflects contemporary climate variability in Central Europe. We suggest replacing traditional classification schemes with multivariate statistics and machine learning methods to create such a new classification. |