1. Introduction

This package provides tools for running quality control (QC) on rain gauge data in a flexible, user-driven way. It is designed to help everyone, from individual researchers to industrial-scale users, apply standardised QC checks to rainfall observations whether you have one rain gauge or a whole network of rain gauges.

At its core, the package offers:

• 27 QC checks for rainfall data as of v0.2.5 (25 from IntenseQC and 2 from pyPWSQC)

• Customizable parameters – adjust thresholds, streak or accumulation lengths, and distances to neighboring gauges

• A modular QC framework – users can select which QC methods to apply, and configure them according to their project’s requirements

This approach allows you to build a tailored QC pipeline: include only the checks you need, set thresholds that match your research purposes, and run consistent, reproducible quality control.

1.1. What type of checks are in the package?

The RainfallQC package breaks down the QC checks into four distinct types:

• Gauge checks – For detecting abnormalities in summary and descriptive statistics.

• Comparison checks – For detecting abnormalities based on rainfall benchmarks.

• Time-series checks – For detecting abnormalities in patterns of the data record.

• Neighbourhood checks – For detecting abnormalities based on measurements in neighbouring rain gauges.

• pypwsqc filters – For applying quality assurance protocols and filters for rainfall data.

These different types of rainfall checks are either rainfall-specific or not and need different amounts of data to run (Figure 1).

Figure 1. Types of checks within RainfallQC

1.2. All QC checks in package

These are the quality control checks currently implemented in the package:

All QC checks

Check	Sub-module	QC Framework	Note
Percentiles	Gauge checks	IntenseQC	QC1
K-largest	Gauge checks	IntenseQC	QC2
Days of week	Gauge checks	IntenseQC	QC3
Hours of day	Gauge checks	IntenseQC	QC4
Intermittency	Gauge checks	IntenseQC	QC5
Breakpoints	Gauge checks	IntenseQC	QC6
Minimum value change	Gauge checks	IntenseQC	QC7
R99p	Comparison checks	IntenseQC	QC8
PRCPTOT	Comparison checks	IntenseQC	QC9
World Record	Comparison checks	IntenseQC	QC10
Rx1day	Comparison checks	IntenseQC	QC11
CDD (Dry spells)	Timeseries checks	IntenseQC	QC12
Daily accumulations	Timeseries checks	IntenseQC	QC13
Monthly accumulations	Timeseries checks	IntenseQC	QC14
Streaks	Timeseries checks	IntenseQC	QC15
Daily neighbours (wet)	Neighbourhood checks	IntenseQC	QC16
Hourly neighbours (wet)	Neighbourhood checks	IntenseQC	QC17
Daily neighbours (dry)	Neighbourhood checks	IntenseQC	QC18
Daily neighbours (dry)	Neighbourhood checks	IntenseQC	QC19
Monthly neighbours	Neighbourhood checks	IntenseQC	QC20
Timing offset	Neighbourhood checks	IntenseQC	QC21
Pre-QC affinity index	Neighbourhood checks	IntenseQC	QC22
Pre-QC pearson correlation	Neighbourhood checks	IntenseQC	QC23
Daily factor	Neighbourhood checks	IntenseQC	QC24
Monthly factor	Neighbourhood checks	IntenseQC	QC25
Faulty Zeros	pyPWSQC filters	pyPWSQC	FZ
Station Outliers	pyPWSQC filters	pyPWSQC	SO