Condensed water vapor in the atmosphere is observed as precipitation whenever moist air rises sufficiently enough to produce saturation, condensation, and the growth of precipitation particles. It is hard to measure the amount and concentration of total precipitation over time due to the changes in the amount of precipitation and the variability of climate. As a result of these, the modelling and forecasting of precipitation amount is challenging. For this reason, this study compares forecasting performances of different methods on monthly precipitation series with covariates including the temperature, relative humidity, and cloudiness of Muğla region, Turkey. To accomplish this, the performance of multiple linear regression, the state space model (SSM) via Kalman Filter, a hybrid model integrating the logistic regression and SSM models, the seasonal autoregressive integrated moving average (SARIMA), exponential smoothing with state space model (ETS), exponential smoothing state space model with Box-Cox transformation-ARMA errors-trend and seasonal components (TBATS), feed-forward neural network (NNETAR) and Prophet models are all compared. This comparison has yet to be undertaken in the literature. The empirical findings overwhelmingly support the SSM when modelling and forecasting the monthly total precipitation amount of the Muğla region, encouraging the time-varying coefficients extensions of the precipitation model.

每当潮湿的空气上升到足以产生饱和、凝结和降水颗粒的增长时，大气中的凝结水蒸气就会被观察为降水。由于降水量的变化和气候的多变性，很难测量总降水量随时间推移的数量和浓度。因此，降水量的建模和预测具有挑战性。出于这个原因，本研究比较了不同方法对月降水序列的预测性能，协变量包括土耳其穆拉地区的温度、相对湿度和云量。为了实现这一点，多元线性回归的性能，通过卡尔曼滤波器的状态空间模型 (SSM)，一种集成逻辑回归和 SSM 模型的混合模型，季节性自回归综合移动平均 (SARIMA)、带状态空间模型 (ETS) 的指数平滑、带 Box-Cox 变换的指数平滑状态空间模型-ARMA 误差-趋势和季节性分量 (TBATS)、前馈神经网络 (NNETAR)和 Prophet 模型都进行了比较。这种比较尚未在文献中进行。在对穆拉地区的月总降水量进行建模和预测时，实证结果压倒性地支持 SSM，鼓励降水模型的时变系数扩展。前馈神经网络 (NNETAR) 和 Prophet 模型都进行了比较。这种比较尚未在文献中进行。在对穆拉地区的月总降水量进行建模和预测时，实证结果压倒性地支持 SSM，鼓励降水模型的时变系数扩展。前馈神经网络 (NNETAR) 和 Prophet 模型都进行了比较。这种比较尚未在文献中进行。在对穆拉地区的月总降水量进行建模和预测时，实证结果压倒性地支持 SSM，鼓励降水模型的时变系数扩展。