Document Type : Original Article
Authors
1
Climatology University of Tabriz
2
Department of Climatology and Urban Planning, Faculty of Planning and Environmental Sciences, University of Tabriz,
3
Department of Climatology and Urban Planning, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran.
4
Department of Climatology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran.
10.30740/cccd.2026.2078059.1089
Abstract
As a prominent climate change hotspot, Iran exhibits complex and heterogeneous hydroclimatic responses to global warming. This study analyzes the temporal dynamics of temperature and relative humidity using monthly data from five representative synoptic stations (Ahvaz, Bandar-e Anzali, Tabriz, Zahedan, and Yazd) over a 38-year period (1986–2023). To model the time series behavior, a comparative framework incorporating Moving Average (MA), Autoregressive Integrated Moving Average (ARIMA), and Holt-Winters exponential smoothing models was employed. Prior to modeling, the distributional structure of the data was assessed using the Anderson-Darling test and graphical probability analysis. The results revealed a consistent and significant warming trend across all stations, confirming a convergent thermal response at the national scale. In contrast, relative humidity exhibited a profound divergence. The most significant finding was the identification of strong evidence for a climate regime shift at the Tabriz station. Here, a bimodal distribution in relative humidity, uncovered through diagnostic analysis, indicates an abrupt transition to a more stable, arid climatic state. Model evaluation confirmed the definitive superiority of the Holt-Winters approach, owing to its capability to adapt to both trend and seasonal components. These findings underscore the insufficiency of standard linear models for assessing climate change in transitioning regions, highlighting that regional adaptation strategies must be formulated based on an understanding of these non-linear and heterogeneous responses.
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