## 1 Introduction  
The Indian economy is mostly reliant on agriculture; hence, annual rainfall is quite important. The summer monsoon, which lasts from June to September, is responsible for the majority of the rainfall. Any variations in rainfall have a significant impact on the agriculture sector and, as a result, the economy of the country. The annual rainfall of Kerala state in India is forecasted in this study. Kerala receives an average annual rainfall of over 3000 mm, which is quite heavy when compared to the Indian average of roughly 1000 mm. The Southwest monsoon is responsible for 70% of Kerala's annual rainfall. Kerala is known as the \"Monsoon Gateway\" since it is the first state in India to receive the rains. Kerala was historically known for its temperate and pleasant climate, owing to its abundant annual rainfall and proximity to the Western Ghats and the Indian Ocean. However, the state has recently felt the brunt of uncertain climate change. Extreme rainfall, floods, landslides, heat waves, sunstrokes, and cyclones have all become increasingly regular in recent years. One of the greatest floods in the state's history struck in 2018, killing hundreds of people and affecting roughly one-sixth of the state's population. Floods and landslides struck Kerala's northern districts again in 2019, causing countless tragedies. Considering this scenario, having a better yearly rainfall prediction model is critical. Many attempts have been made in India to predict rainfall at the regional and national levels. Probabilistic and deterministic methods such as ARMA-based methods were used to predict rainfall usingthe hydrological datasets. However, these approaches have limitations such as nonlinearity and non-stationarity. The primary objective of this work was to predict the annual rainfall of Kerala based on the rainfall time series data from 1871 to 2020. The data for this period are available on the websites of the Indian Institute of Tropical Meteorology and the Indian Meteorological Department. Annual rainfall data are very nonlinear and non-stationary in time series. This makes statistical methods to make a forecast challenging. This work proposes empirical mode decomposition based on random forest regression to predict the annual rainfall. The suggested model was compared to the ARMA and RF regression models to assess its performance. Mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination or R-squared (R\({}^{2}\)) values were used to evaluate the forecasting ability of these three models. Several studies have been conducted to investigate long-term as well as short-term rainfall prediction using conventional statistical and machine learning methods. Support vector regression (SVM) and multilayer perception (MLP) were used for the annual rainfall prediction of Odisha, India. The SVM model exhibited better performance than MLP with an R-value of 0.9 [1]. Artificial neural networks-multilayer perception was used to forecast 15 years of rainfall across India using rainfall data from 1901 to 2015. The work investigated the variability and predicted the trend of rainfall in various regions of India [2]. Hybrid models were used for monthly rainfall prediction in Vietnam, where the time-series data were decomposed using two pre-processing methods--seasonal decompositions and discrete wavelet transform. This pre-processed data were fed to artificial neural networks (ANN) and seasonal ANN prediction (SANN) models. The work found out that [PERSON] wavelet-SANN model gave the best prediction with R-value, MAE, and RMSE as 0.997, 9.32 and 12 mm, respectively [3]. The inter-annual variability of India's Southwest monsoon rainfall was investigated using empirical mode decomposition and forecasting modeling was carried out using ANN. All India rainfall for the year 2004 and its standard deviation were forecasted as 80.34 and 3.3 cm, respectively [4].