Which Machine Learning Choices Matter Most for Rainfall Prediction? It Depends on the Climate

Machine learning has become the default toolkit for predicting rainfall, but a new study argues that the field has been asking the wrong question. Instead of hunting for a single best algorithm, researchers at the University of Tehran set out to determine which of the many decisions in a precipitation forecasting workflow actually drive performance — and the answer, they found, changes dramatically depending on how wet or dry the basin is. The work, published in Earth Science Informatics, applies a rigorous statistical framework known as factorial analysis of variance, or ANOVA, to disentangle the contributions of four fundamental modeling components: outlier detection, missing-value imputation, model selection, and the length of the training record.

The motivation is straightforward. Hydrologists building machine learning models for precipitation prediction face a cascade of choices before a single forecast is produced. Should suspicious values in the raw record be flagged with classical statistical tests or with modern density-based algorithms? How should gaps in the data be filled? Which learning algorithm deserves the effort of hyperparameter tuning? And does adding another decade of historical data genuinely help? Most studies compare these ingredients one or two at a time within a single catchment, leaving open the possibility that conclusions drawn in one climate simply do not transfer to another. The new research tackles this gap head-on by running a full factorial experiment across five Iranian basins that span markedly different precipitation regimes.

The data underpinning the study come from ERA5, the European Centre for Medium-Range Weather Forecasts’ flagship global reanalysis product, which provides gridded monthly precipitation estimates from 1990 through 2023. Reanalysis products like ERA5 blend observations with numerical weather model output, offering a consistent long-term record even in regions with sparse ground instrumentation. The researchers deliberately framed the forecasting task in a simple, reproducible way: one-step-ahead prediction of monthly precipitation using the three preceding monthly values and the month number as predictors. This minimalist setup ensures that differences in performance can be attributed to the modeling components under investigation rather than to an elaborate feature-engineering pipeline.

The experimental design is where the study earns its statistical teeth. Eight different outlier-detection methods were applied to each basin’s precipitation series, ranging from classical procedures such as Grubbs’ test and Tukey’s fences to robust approaches like the Hampel filter and density-based techniques including the Local Outlier Factor and Isolation Forest. Six imputation methods were then used to fill any missing values. On top of each resulting dataset, the team trained four machine learning models representing distinct families of algorithms: Random Forest, an ensemble of decision trees; XGBoost, a gradient-boosting system known for its efficiency and accuracy on tabular data; Gaussian Process Regression, a Bayesian non-parametric method; and the Multi-Layer Perceptron, a classic feedforward neural network. Each model was trained on three different record lengths — 12, 22, and 32 years of monthly data — producing a large grid of modeling configurations for every basin.

Crucially, the evaluation protocol respected the temporal structure of the data. The final three years of each series were held out as an independent test period, untouched during model development. Hyperparameter optimization was performed exclusively within the training data using TimeSeriesSplit, a cross-validation scheme that preserves chronology, and the tuning was carried out separately for two objective functions: one maximizing the Nash–Sutcliffe Efficiency (NSE) and one maximizing the Kling–Gupta Efficiency (KGE). These two metrics capture different aspects of forecast quality — NSE emphasizes the match of variances around the observed mean, while KGE decomposes performance into correlation, bias, and variability — so optimizing for each separately guards against conclusions that hinge on the choice of score.

The headline result is a striking dependence of component importance on climate. In the humid Haraz basin, the choice of machine learning model was overwhelmingly the dominant factor, accounting for 82.0 percent of the variance in NSE and 59.9 percent of the variance in KGE. In other words, in a wet basin where precipitation is abundant and relatively well-behaved statistically, picking the right algorithm matters far more than any preprocessing decision. As mean precipitation decreases, however, the picture inverts. In the drier basins, outlier-removal methods rose to prominence, contributing up to 31.5 percent of the NSE variance and as much as 54.5 percent of the KGE variance — meaning that in arid settings, how you clean the data can matter as much as or more than which model you choose.

This pattern has a plausible physical explanation. In dry regions, monthly precipitation is low, episodic, and heavily skewed, so a handful of extreme or anomalous values exert an outsized influence on the training signal. An outlier-detection method that aggressively removes or downweights such points can reshape the entire learning problem, whereas in a humid basin the signal is strong enough that individual anomalies barely register against the background of frequent, substantial rainfall. The finding suggests that data-cleaning pipelines should be treated as climate-sensitive design choices rather than one-size-fits-all preprocessing steps.

Two other results will surprise practitioners. First, the choice of imputation method — how missing values are filled — generally made a negligible contribution to performance variance across the basins. The lone exception was the Aras basin under the KGE metric, where imputation accounted for 16.2 percent of the variance, a reminder that even broadly negligible factors can matter in specific climatic and metric contexts. Second, and perhaps most counterintuitively, the length of the training record showed no consistent direct relationship with improved predictive performance. Adding ten or twenty extra years of data did not reliably produce better forecasts, challenging the common assumption that more history is always better in hydrological machine learning.

The methodological lesson is that factorial ANOVA offers a powerful, underused lens for machine learning research in the geosciences. By treating each modeling decision as a factor in a designed experiment and quantifying the share of performance variance each factor explains, researchers can move beyond leaderboard-style comparisons of individual algorithms and toward a principled understanding of where modeling effort is best spent. The approach also naturally exposes interactions between components — for instance, whether the benefit of a particular outlier filter depends on which algorithm is downstream of it — which pairwise comparisons tend to obscure.

For water managers and forecasters, the practical takeaway is that there is no universally optimal recipe for precipitation prediction. A workflow tuned in a temperate, humid catchment may misallocate effort entirely if transplanted to an arid one, where data cleaning deserves the lion’s share of attention. The authors argue for basin-specific, climate-aware modeling strategies: diagnose the precipitation regime first, then decide whether algorithm selection or data preprocessing is the higher-leverage investment. As machine learning spreads deeper into hydrology and water resources management, studies like this one suggest that the most valuable question is not which model is best, but which decision, in this place and this climate, actually moves the needle.

Subject of Research: Relative importance of machine learning modeling components for precipitation prediction across contrasting climatic basins

Article Title: Assessing the relative importance of machine learning modeling components for precipitation prediction using ANOVA

Article References: Dalir Gabrabad, A., Hosseinpour, M., & Ashrafzadeh, A. (2026). Assessing the relative importance of machine learning modeling components for precipitation prediction using ANOVA. Earth Science Informatics, 19(11), Article 204. https://doi.org/10.1007/s12145-026-02260-1

Image Credits: AI Generated

DOI: 10.1007/s12145-026-02260-1

Keywords: machine learning, precipitation forecasting, ANOVA, outlier detection, missing data imputation, Random Forest, XGBoost, Gaussian Process Regression, Multi-Layer Perceptron, ERA5 reanalysis, Nash-Sutcliffe Efficiency, Kling-Gupta Efficiency