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There are so many things that can alter the answer to your question…. Some places have much more variability in their weather patterns… to the degree that the same month one year may be 5 rain days and another year it’s 20 rain days, the average of rain days will be the number of rain days each year averaged over a period of time (maybe 10 years) so in this example the average might be 12 rain days but it may be as many as 20 days during several years when a particular weather pattern is prevalent.

Things that alter the local weather pattern are things like altitude of the place and height of surrounding mountains, major high altitude air currents like the jet stream, proximity to the coast and sea temperature, proximity to seawater currents like the Gulf Stream, low level winds like the trade winds, ares of desert, proximity or not to the equator and position within a large landmass all create local pockets of weather patterns that are unique often to a very small geographic area. So where you are the answers may be very different compared to where I am.

Some places are even “high or low” air pressure generators that begin to propagate patterns of weather in different ways as the wind direction changes. So if winds are easterly it’s pattern “A” if winds are westerly it’s pattern “B” if winds are very low then pattern “C” becomes more likely.

A weatherman will probably tell you, you are far less likely to see the average number of rain days or the average temperature occur because the average is just that the number calculated mathematically. Now that may be a good average because there is never much variation or a bad average calculated from two extremes… normally weathermen know that in some places it will regularly be very high or very low, the average itself May happen very very rarely.

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This is not a direct answer to the part of your question about the degree to which weather systems within a short period of time can be "linked", but with respect to the difference between a given year and an average value, there are two points to consider.

1. Something like the average you mention, i.e., "5 wet days per month" is just that, an average. For most climatological parameters, a reported mean will represent at least 10 years of data (usually more). Importantly, the mean only tells you about the central tendency, but nothing of the variability. If we were dealing with normally distributed data, you could think about something like the standard deviation as a crude metric of variability. So for your given area, if the average wet days for your month of interest was 5 and had a standard deviation of 1, that would broadly suggest 14 wet days is a lower probability event, but if the standard deviation was 5, that would similarly broadly suggest that the precipitation in that month is more variable (assuming you had enough a long enough set of data where you standard deviations were meaningful). Now in reality, while some climatological variables tend to be close to normally distributed (e.g., temperature), precipitation tends to not be well explained by normal distributions. Instead, distributions like the weibull, logistic, exponential, or GEV are better suited for describing precipitation. For these, we might be interested in the "shape" parameter (or equivalent) for a fit to the distribution of rainfall assuming one of these distributions which would give us a sense of the variability and thus how probable a significant deviation from the mean is.
2. In a similar theme, mean climatological parameters will typically represent averages across a variety of cyclical changes in climate, things like ENSO. We are currently experiencing La Niña, so hypothetically, if for your location in this month, it's typically more wet during La Niña (and perhaps drier during El Niño, where the mean reflects somewhere between the two), then this may not be "out of the ordinary" at all.

It depends on the region.

The tropics for instance have somewhat more consistent and predictable patterns over, say, a couple of months, including deviations to the "normal" climate. This is because the weather is heavily influenced by cyclic phenomena such as El Nino (you may have heard that name in the media) that have worldwide repercussions.

On the other hand the weather in the mid-high latitudes is much more chaotic and hardly predictable beyond 2 weeks. The atmospheric patterns are similar to waves. Depending on you being at the bottom or the top of the wave, you may experience warmer or colder weather, or more or less rain. Also if some place has a negative temperature anomaly, chances are that another place a couple thousand km away (that the scale we are talking about) has just the opposite.

The waves are moving spatially (usually west to east). So to some extend after a rainy event you can expect sun to come back. The problem is that waves can also be stationary (e.g. due to a high pressure area acting as a roadblock), and generally we struggle to predict how they will move beyond these two weeks.

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This is where we have to start thinking of weather in a probabilistic sense. How likely is an event to occur within the statistical framework of the recorded climate (official climate record is typically 30 years in the US)? This is where we get the things like 1000 year floods, 100 year heatwaves, etc. In the vast majority of weather systems, energy moves downscale. What I mean by this is that big weather systems driven by large scale processes then drive smaller systems which themselves drive even smaller ones. When two large scale events line up, like ENSO (el nino/la Nina) and typical winter time jet stream, you may see an event that is far outside the statistical norm within the climatological record.

So I guess we could say short term weather patterns can be connected together, but large scale and longer time scale patters will be more influential.

The single best answer to your question should be, "It depends."

It's clear. It's succinct. It's complete.

You can, and should, find as many fields of weeds on this subject to get lost in as you can. And there are hundreds. Find some weeds and go play.

But you won't find a better answer. Sorry.

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To add to this, there are large-scale structures that DO make excellent predictors of weather to come. For instance, a "bomb cyclone" just hit the western coast of California. These mid-latitude cyclones follow somewhat predictable patterns.

Over the US, winds tend to blow from the west, especially along the Pacific coast, so it was pretty much guaranteed that this weather system would move from west to east. In that link you will see a satellite picture of the storm, which has the tell-tale cyclone shape, like a comma with two tails. The clouds on the western tail form a narrow band, which is indicative of the tall storm clouds associated with a cold front. The eastern tail is wide, made of low-hanging stratus clouds that form due to an incoming warm front. In the center of the comma is a swirling occluded front where the cold and warm fronts meet.

Experiencing a few days of little precipitation and overcast skies does not necessarily tell you a whole lot about the weather to come. But if you know that the large scale formation looks like this cyclone and you experience those overcast days followed by a couple clear days, then on the clear days you can bet that you are in the cloudless space between the comma tails, so the cold front is coming, bringing with it heavy rain.

But as HankScorpio here said, using monthly averages as a prediction tool is not a great idea, simply because of the potential variability of year-to-year climates.

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This is how I understand your question:

If the average rain per month is X and for the first 2 weeks there have already have been X rain, does this mean that the rain probability for the next 2 weeks would be higher or lower?

There is no easy answer. There is no monthly rain quota where you used up your monthly rain early. One will have to analyze the reasons why there was more rain than usual.

Still, globally, I would be inclined to say that if the first part of the month was not normal, there is somewhat higher than average probability that the second won't be either. Weather patterns come from global cycles and global weather systems - and it in this case something is off. For example the European weather is highly influenced by the current state of the Azores High and the Icelandic Low. If these are off for some reason, it usually takes some time before they gradually return to normal.

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The weather system is a complicated example of what's known as a chaotic system in mathematics. Most people have heard of the "butterfly effect", or the idea that a tiny perturbation of a system will exponentially grow into large-scale differences of behavior over time. This fundamentally limits our ability to predict the future with confidence.

An important parameter of any chaotic system is the Lyapunov time, which is the timescale over which perturbations grow large. The weather is a very complicated system, but current estimates for its Lyapunov time are around 15 days. Thus, any knowledge you have about particular weather conditions today won't inform your predictions very much for the weather in 15 days or so.

That being said, weather is driven by predictable factors like solar input, seasons, ocean currents, land topography, and so on. So at any given point on the Earth you can make some prediction of what the temperature, precipitation, and so on are likely to be on a given date, based on historical data. Those climate averages give you some degree of predictability.

So a way to think about it is:

• Weather forecasters are pretty good at telling you what will happen tomorrow.
• When they look out to 21 days from now, they can only predict based on historical climate data.
• Between "tomorrow" and "21 days from now", the accuracy of predictions gradually declines, because of chaos as well as limitations in the quality of our data and models.

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It is difficult to say with certainty what the weather will be like in the coming weeks based on the weather that has occurred in the past. Weather patterns can change over time and can be influenced by a variety of factors such as temperature, humidity, and atmospheric pressure.

That being said, if it has been raining for two weeks straight and the average monthly climate for that place typically only has five rainy days, it is possible that the weather in the coming weeks may be less rainy. However, this is not guaranteed and the weather could continue to be rainy or could become more or less rainy for any number of reasons. Ultimately, it is always best to be prepared for a variety of weather conditions and to stay informed about the local weather forecast.

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So for the temperature anomaly, would North America / Europe recently be an example of this? Negative anomaly in America and positive in Europe?

What you are seeing these days, that feels like a short term - once in a life time weather - is actually caused by climate change - so there is nothing random about it, it is fully man made.

The freeze of the US over the past weeks is cause by shifting jet streams which is allowing and pulling more of the cold air from the noth pole down. In return the north pole have gotten a little bit warmer because, hey you got the cold air instead, and in return you will find that more ice will melt.

The extremes rains on the west coast, is also caused by warming of the pacific oceans all the way over by the Philippines, when the ocean is just 1-2F warmer it evaporates significantly more ocean water, and that then drops when it reaches land.

So the "paradox" is that the same global warming can result in climate change in different ways, causing weather to be "weird" like freezing in the midwest, and extreme rain in the west.

I'm sure we could find plenty of other examples - but the pattern is the same - the weather is going to be more extreme in different ways, with each region being affected differently.

There are large systems and cycles that impact our weather.

There are ocean oscillations that vary due to sea temperature, air pressure, and wind and often have periods in positive or negative phases.

The jet streams and Hadley Cells can also impact our weather. See this link from NWS to read about how these work.

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Maybe the simplest answer to your question is that weather patterns are chaotic.

The concept of chaos is not difficult to understand. A system is chaotic if a very tiny change in its state can cause a major change to its state further down the road. There are a bunch of videos on Youtube of double pendulums - this is one of the simplest chaotic systems.

So, weather is difficult to predict. Weather forecasters can generally do a pretty good job of predicting the weather 3 days away, but more than that, and it gets a bit dicey. Having said that, there are macro patterns like El Nino/La Nina which can affect the weather for a long period of time.

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Numerous variables, such as regional topography, long-range atmospheric circulation patterns, and the movement of weather systems, can have an impact on short-term weather patterns. Since there are so many factors that might change the weather from day to day or week to week, it is typically challenging to accurately predict short-term weather patterns. Having said that, it is possible that an unusually prolonged spell of rain in one place may have been caused by a bigger weather pattern that is affecting the area. For instance, the existence of a low-pressure system that is pushing humid air into the area may be the cause of a location experiencing a prolonged stretch of wet weather. In this instance, it's likely that the rainy weather will prevail for the duration of the month, yet it's also possible that it will turn drier.

Since there are so many factors that might change the weather from day to day or week to week, it is typically challenging to accurately predict short-term weather patterns. In general, for the most precise and current information on the weather in a certain location, it is better to rely on weather forecasts from trustworthy sources, such as the National Weather Service.

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Nothing happens in weather at random, it's s just so complicated it seems that way. Short term weather fluctuations happen and appear to be more random because there's no powerful and sustained force holding it together for a longer period of time. That means different factors are able to enter a particular geographic area and fight with competing forces, often switching it up throughout the week or throughout the day.

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