<![CDATA[Rainfall variability strongly influences rain-fed agricultural activities in monsoonal regions, particularly across seasonal and spatially varying rainfall regimes. This study develops and applies a climatology-relative framework for evaluating rainfall-condition categories derived from observed and Random Forest (RF)-predicted dasarian rainfall for Besuki Na-Oogst tobacco in ZOM 45–49, Jember, East Java. A 30-year historical rainfall dataset (1993–2022) was used to derive ZOM- and dasarian-specific P25 and P75 relative rainfall-condition thresholds. The same fixed thresholds were then applied to observed and RF-predicted rainfall throughout January–December 2024 to classify rainfall into Dry, Normal, and Wet categories. Categorical correspondence was evaluated using percentage agreement, Cohen's kappa, and a 3 × 3 confusion matrix at the ZOM × dasarian level. Across 180 ZOM × dasarian pairs, observed rainfall produced 50 Dry, 84 Normal, and 46 Wet classifications, whereas RF-predicted rainfall produced 7 Dry, 68 Normal, and 105 Wet classifications. Overall categorical agreement was 38.33% (κ = 0.071), with variation among the five ZOMs. The most pronounced discrepancy occurred in the Dry category: none of the 50 observed Dry cases were reproduced as Dry by the RF predictions, while 36 were classified as Wet. Conversely, all seven RF-predicted Dry cases corresponded to observed Wet conditions. The continuous comparison indicated that the RF predictions generally reproduced the broad seasonal rainfall pattern but tended to exhibit smoother variations and, in several periods, lower rainfall magnitudes than observed. These differences can lead to categorical reversals when predicted values fall across fixed climatological P25 and P75 boundaries. The findings indicate that RF-derived rainfall-condition patterns may provide complementary information for climatology-relative assessment, but should not be interpreted as direct substitutes for observed rainfall conditions or as standalone planting recommendations. The proposed framework complements conventional numerical rainfall-prediction evaluation by assessing the categorical representation of predicted rainfall within a common local historical climatology at the ZOM × dasarian scale.]]>
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