Climate change (CC) significantly influences agricultural water productivity, necessitating increased irrigation. Therefore, the present study was undertaken to assess the trend and change-point analyses of weather variables such as temperature (T), rainfall (R), and reference evapotranspiration (ET0) using 31-year long-term data for semi-arid climate. The analysis was carried out employing Mann-Kendall (MK), Modified Mann-Kendall (MMK), Innovative Trend Analysis (ITA), and Innovative Polygon Trend Analysis (IPTA) methods. Homogeneity tests, including Pettitt's test, Standard Normal Homogeneity Test (SNHT) , Buishand range test, and Von Neumann Ratio Test (VNRT), were employed to detect change points (CPs) in the time series data. The results indicated that, for maximum temperature, MK and MMK revealed a positive trend for September and July, respectively, while minimum temperatures indicated increasing trends in August and September. Rainfall exhibited an increasing trend during the Zaid season (April-May). ET0 exhibited a negative trend in January. ITA and IPTA displayed a mixture of positive and negative trends across months and seasons. The change-point analysis revealed that for Tmax, the CP occurred in 1998 for time-series data for the month of April. Likewise, for Tmin, the change points for April and August time series were found in 1997. HIGHLIGHTS center dot Four trend and four change-point analysis techniques were employed for studying temperature, rainfall, and ET0. center dot The maximum and minimum temperatures revealed significant trends in the monthly data series. center dot There was a rising trend in the rainfall in the Zaid season (April-May) as well as the annual average rainfall. center dot The ET0 showed a decreasing trend in January. center dot ITA and IPTA showed a mix of positive and negative trends across months and seasons.