# Homogeneity and Trend Analysis of Climatic Variables in Cap-Bon Region of Tunisia
## 1 Introduction  
Atmospheric carbon dioxide rates have increased significantly since the 1950s, a phenomenon that affects global and regional climate characteristics, such as temperature and precipitation [1]. The effects of climate change are evident directly in terms of extreme events, such as droughts, floods, water resource availability and agricultural production, and indirectly via increasing crop water requirements [2]. Over the last century, the average global air temperature has increased by about 0.74 \({}^{\circ}\)C [3]. It is worth mentioning that the observed warming, as well as precipitation anomalies, is not globally uniform [4; 5]. [PERSON] and [PERSON] [6] highlighted that temperature variations at regional scales may be muchmore pronounced and that substantial spatial and temporal variations can be identified between climatically different areas. A long-term time-series trend analysis of meteorological parameters is a primordial step in climate change studies. Several studies have been conducted to assess trends in temperature and precipitation across the globe [7; 8; 9; 10; 11]. [PERSON] et al. [12] assessed the trend of precipitation and the accuracy of predicted temperatures at three stations in Pakistan, for the period of 1960-2020, based on the non-parametric Mann-Kendall test. The obtained results revealed the existence of a positive trend in annual precipitation. However, a negative trend was observed in monthly precipitation, and the trend was statistically significant for two stations. [PERSON] et al. [13] analyzed 120-year precipitation time series for three Italian meteorological stations (Florence, Pisa and Palermo). Their results showed a decreasing trend in annual precipitation at Pisa and Palermo and a highly significant downward trend in rainy days at Florence and Pisa. For most Mediterranean regions, a significant decrease in precipitation associated with an increase in exceptional phenomena, such as severe droughts, has been detected [14]. [PERSON] et al. [7] examined the changes in annual and monthly temperatures, rainfall and potential evapotranspiration time series in a part of southwestern France. Their findings highlighted that the annual mean temperature and potential evapotranspiration significantly increased; however, no significant trend was detected in annual precipitation. In addition, an important seasonal variability in the trends of all the studied climatic variables was recorded. [PERSON] et al. [15] analyzed the trends in annual air temperatures and precipitation time series in different regions in Iran during the 1966-2005 period. They observed an upward trend in air temperature at most of the stations, while a mixed tendency of the precipitation data was recorded. According to [PERSON] et al. [16], the specific humidity, solar radiation and minimum temperature all had a negative impact on the trend of reference evapotranspiration in Sicily, but the maximum temperature, mean temperature and wind speed all had a favorable impact. They also discovered that there was no annual trend for any of the factors studied. Trends are only observed on a monthly or seasonal scale. Except for the minimum temperature and reference evapotranspiration (ET0), all variables showed significant trends at the monthly and seasonal time scales. Monthly ET0 showed a significant trend for November, and the monthly minimum temperature showed a significant trend for August and September. Numerous statistical tests classified as parametric and non-parametric tests are widely employed to check whether either the data of a given set present an abrupt change in the distribution or have a significant trend [9; 17]. It is worth mentioning that parametric tests are better than non-parametric ones and that they require the data to be independent and normally distributed [18]. Meanwhile, non-parametric tests are less sensitive for non-normal distribution, extreme values and missing data, and they require the data to be independent [11].